Deforestation

Data Science for Business Analytics Project

Introduction

Overview and Motivation

According to the World Wildlife Fund forests cover more than 30% of the Earth’s land surface and can provide food, medicine and fuel for more than a billion people. They are a resource, but they are also large, undeveloped land that can be converted for purposes such as agriculture and grazing. According to National Geographic in North America, about half the forests in the eastern part of the continent were cut down for timber and farming between the 1600s and late 1800s, today, most deforestation is happening in the tropics. Negative effects of deforestation are the loss of animal and plant species due to their loss of habitat, the increase of greenhouse gases and lower water levels.Trees are bringing humidity to the land, by decreasing the trees numbers also the humidity in the land of the region decreases this leads to a higher risk of forest fire. Because the negative effects of deforestation are substantive we decided to focus in our project on the areas that are affected from deforestation the cause of deforestation.

Related Work

Some studies also identified the causes of deforestation, like the effect of agriculture. According to Arima et al. (2011) a 10% reduction of soy in old pasture areas would have decreased deforestation by 40%. Pfaff et al. (2007) investigate links between road networks and deforestation, Carr and Burgdorfer (2013) investigate the implications of rural populations and other authors like Ehrhardt-Martinez, Crenshaw, and Jenkins (2002) investigate links between environmental Kuznets curve and deforestation. Faria and Almeida (2016) explore to impact of openness to trade and Gosh H. showed that mining is one of the main causes responsible for deforestation in India.

Research questions

Therefore we developed the following research questions: Which countries are affected by the deforestation? How does this differ by year? What are the causes of deforestation? Which of these causes have the highest effect?

Data

Sources

• AgriLandData : https://data.worldbank.org/indicator/AG.LND.AGRI.K2

• ForestAreaData : https://data.worldbank.org/indicator/AG.LND.FRST.K2

• AgriVarData : https://data.worldbank.org/topic/trade

• ResourcesExtractionData : https://www.eia.gov/international/data/world

• PopGrowth : https://data.worldbank.org/indicator/SP.POP.GROW

• TradeOpenessData : https://ourworldindata.org/grapher/trade-openness

• SoybeansData : https://www.fao.org/faostat/en/#search/soy

• LandAreaData : https://data.worldbank.org/indicator/AG.LND.TOTL.K2

Index for the different variables

In our analysis we’ll be using many variables. Some of them are directly taken from the original data sets and some of them are derived from existing variables. The aim of this “Data” section is to tidy one by one our data set and extract the variables we are interested in. Here we’ll state an index to easily find where we treated each variable of our final “merged” data set:

1. Agricultural land

2. Change in agricultural Land

3. Forest area

4. Deforestation

5. Agricultural exports

6. Total land area

7. Production / Extraction of sources of energy

8. Population growth

9. Openness to trade

10. Soybeans production

11. Merged Data Set

1) Agricultural Land

The “AgriLandData” data set shows the total area of agricultural land in square km per country/region for the years between 1960 and 2020. In this project we will limit our analysis the years starting from 2001. This data set is taken across 266 countries.

Country	Year	AgriLand
Aruba	1980	20
Aruba	1981	20
Aruba	1982	20
Aruba	1983	20
Aruba	1984	20
Aruba	1985	20
Aruba	1986	20

2) Change in Agricultural Land

To know how the area of agricultural land changed over time we created two new columns that contain information about the change to the year before, one in square km and one in percentage.

Country	Year	AgriLand	Change agricultural land	Change agricultural land %
Africa Eastern and Southern	1981	5391340	400	0.007
Africa Eastern and Southern	1982	5391740	4280	0.079
Africa Eastern and Southern	1983	5396020	-40	-0.001
Africa Eastern and Southern	1984	5395980	21580	0.400
Africa Eastern and Southern	1985	5417560	7640	0.141
Africa Eastern and Southern	1986	5425200	3470	0.064
Africa Eastern and Southern	1987	5428670	5780	0.106
Africa Eastern and Southern	1988	5434450	11010	0.203

3) Forest Area

The *ForestArea" data set shows the total forest area in square km per country/region for the years between 1960 and 2020. In this project we are just interested in the years starting from 2001.

Country	Year	ForestArea
Africa Eastern and Southern	1990	4988232
Africa Eastern and Southern	1991	4968316
Africa Eastern and Southern	1992	4947641
Africa Eastern and Southern	1993	4911135
Africa Eastern and Southern	1994	4890839
Africa Eastern and Southern	1995	4870543
Africa Eastern and Southern	1996	4850247
Africa Eastern and Southern	1997	4829951

4) Deforestation

Using the “ForestAreaData” data set, we created two new columns that contain information about the change to the year before, one in square km and one in percentage, namely the “Deforestation” variable and the “Deforestation %”

Country	Year	ForestArea	Deforestation	Deforestation %
Africa Eastern and Southern	1990	4988232	-19916	-0.004
Africa Eastern and Southern	1991	4968316	-20675	-0.004
Africa Eastern and Southern	1992	4947641	-36506	-0.007
Africa Eastern and Southern	1993	4911135	-20296	-0.004
Africa Eastern and Southern	1994	4890839	-20296	-0.004
Africa Eastern and Southern	1995	4870543	-20296	-0.004
Africa Eastern and Southern	1996	4850247	-20296	-0.004
Africa Eastern and Southern	1997	4829951	-20296	-0.004

5) Agricultural exports

The data set we took includes information about 149 indicators for 237 countries from 1960 to 2020. To get an impression about the data we show the first 7 rows and 7 columns from the data set.

Country	Indicator Name	1960	1961	1962	1963	1964
Aruba	Merchandise exports by the reporting economy, residual (% of total merchandise exports)	NA	NA	NA	NA	NA
Aruba	Merchandise exports to low- and middle-income economies in Sub-Saharan Africa (% of total merchandise exports)	NA	NA	NA	NA	NA
Aruba	Merchandise exports to low- and middle-income economies in South Asia (% of total merchandise exports)	NA	NA	NA	NA	NA
Aruba	Merchandise exports to low- and middle-income economies in Middle East & North Africa (% of total merchandise exports)	NA	NA	NA	NA	NA
Aruba	Merchandise exports to low- and middle-income economies in Latin America & the Caribbean (% of total merchandise exports)	NA	NA	NA	NA	NA
Aruba	Merchandise exports to low- and middle-income economies in Europe & Central Asia (% of total merchandise exports)	NA	NA	NA	NA	NA
Aruba	Merchandise exports to low- and middle-income economies in East Asia & Pacific (% of total merchandise exports)	NA	NA	NA	NA	NA
Aruba	Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)	NA	NA	NA	NA	NA

Before selecting the variables we need we changed the structure of the data set such that it has the year and the different indicator names as column names and we also just selected data from 2000 and above.

Country	Year	Travel services (% of commercial service exports)	Transport services (% of commercial service exports)	High-technology exports (% of manufactured exports)	High-technology exports (current US$)
Aruba	1980	NA	NA	NA	NA
Aruba	1981	NA	NA	NA	NA
Aruba	1982	NA	NA	NA	NA
Aruba	1983	NA	NA	NA	NA
Aruba	1984	NA	NA	NA	NA
Aruba	1985	NA	NA	NA	NA
Aruba	1986	77.3	2.98	NA	NA

From all these variables just two are of interest for our project. Therefore we selected just these indicators and created a new data set for each of them. The result was:

Country	Year	Merchandise exports (current US$)
Albania	1992	70000000
Albania	1993	122000000
Albania	1994	140000000
Albania	1995	202000000
Albania	1996	207000000
Albania	1997	139000000
Albania	1998	205000000
Albania	1999	351000000

A data set that shows the percentage of food exports from total merchandise exports in US$ per country and year.

Country	Year	Food exports (% of merchandise exports)
Aruba	2000	49.96
Aruba	2001	45.08
Aruba	2002	44.09
Aruba	2003	46.87
Aruba	2004	35.86
Aruba	2005	1.18
Aruba	2006	34.38

And now we’ll also take a data set showing the percentage agricultural raw material exports from total merchandise exports in US$ per country and year.

Country	Year	Agricultural raw materials exports (% of merchandise exports)
Aruba	2000	0.737
Aruba	2001	0.549
Aruba	2002	0.993
Aruba	2003	1.172
Aruba	2004	1.249
Aruba	2005	0.317
Aruba	2006	1.096

6) Total Land Area

This data set shows how much squared kilometer every country has. This will make it possible identify how many percent of this area is forest and how many percent of the land is agricultural land.

Country	Year	Land Area in sq km
Aruba	1980	180
Aruba	1981	180
Aruba	1982	180
Aruba	1983	180
Aruba	1984	180
Aruba	1985	180
Aruba	1986	180

7) Extraction of Minerals Data

This data set contains the different amount of extraction of different sources of energy taken worldwide. Although it was a difficult data set to find for the whole world, it was also a hard one to tidy and here is why.

After running the four first line of code in order to deleat the unnecessary information lines and name the columns to be able to use it, we end up to this stage:

ExtractVar	1980	1981	1982	1983
Algeria
Production (quad Btu)	2.803017355026457	3.0375368604701833	3.224933778884667	3.6064004828010217
Coal (quad Btu)	0.0000759200610490114	0.00024041352665520277	0.00040490699226139416	0.0005694004578675855
Natural gas (quad Btu)	0.48498	0.91096	1.11156	1.55052
Petroleum and other liquids (quad Btu)	2.3153852101494885	2.122552459995088	2.1080136736977853	2.052859918102554
Nuclear, renewables, and other (quad Btu)	0.00257622481592	0.00378398694844	0.00495519819462	0.0024511642406
Nuclear (quad Btu)
Renewables and other (quad Btu)	0.00257622481592	0.00378398694844	0.00495519819462	0.0024511642406

Now we can see our problem: The name of the country appear as a title that is in the same column as the variables names we need. We are therefore at this stage unable to pivot our tibble. In order to solve this problem we created a new column “Country” where we take only one row every eights of the first column and the copy it for all the variable. Then we deleted the row corresponding at the country name that is now store in the column “Country” and obtain something like this:

Now we are finally able to pivot our tibble to have a tidy data set usable for the rest of our analysis:

Country	Year	Production (quad Btu)	Coal (quad Btu)	Natural gas (quad Btu)	Petroleum and other liquids (quad Btu)	Nuclear, renewables, and other (quad Btu)	Nuclear (quad Btu)	Renewables and other (quad Btu)
World	1980	296.21435254624544	79.9919425299794	54.761045594	133.11110886607943	28.350255556186585	7.575700462108056	20.77455509407853
World	1981	291.2691434621497	80.44275739096139	55.573536747	125.4389494908981	29.81389983329021	8.527153469041346	21.286746364248867
World	1982	290.16459397739266	83.44334341199846	55.495522664	119.76280547269802	31.46292242869618	9.50768642751678	21.9552360011794
World	1983	293.0884301960975	83.72816748576506	56.115951591	119.26919636784994	33.97511475148248	10.718344095623602	23.25677065585888
World	1984	308.88419722061724	87.63254400981725	61.758597532	122.55382660121982	36.93922907758015	12.994607878980153	23.9446211986
World	1985	316.3867487746443	91.60218713955003	64.124522824	121.1347896143825	39.52524919671177	15.298615144911766	24.2266340518
World	1986	326.69952978342883	93.99118261682449	65.3286482	126.54884517346557	40.830853793138765	16.247905140338766	24.5829486528

8) Population Growth Data

This data aims to measure the population growth derived from the total population. We’ll be using this variable to determine a potential link with the deforestation. In order to tidy it we got rid of the unnecessary lines and once we had our column name set to the years, we pivoted our tibble to have all years as one column namely one variable. The data is taken as percentage of growth compare to the previous year and are taken from year 1960 to 2020 across 266 different countries.

Country	Year	PopulationGrowth
Aruba	1980	0.208
Aruba	1981	0.769
Aruba	1982	1.280
Aruba	1983	1.412
Aruba	1984	0.981
Aruba	1985	0.315
Aruba	1986	-0.603

9) Openness to Trade Data

In order to capture whether a country is open to trade or not, the best proxy variable that we found we to take the ratio of export and import over de GDP, again taken each year for each country.

Country	Year	Export/Import of GDP
Afghanistan	1980	25.9
Afghanistan	1981	25.7
Afghanistan	1982	26.0
Afghanistan	1983	25.8
Afghanistan	1984	25.8
Afghanistan	1985	25.9
Afghanistan	1986	25.9

10) Soybeans dataset

This data set represent the amount of soybeans produced per year for each country.

Country	Year	Gross Production Value Soybeans
Albania	1993	151
Albania	1994	31
Albania	1995	44
Albania	1996	131
Albania	1997	76
Albania	1998	49
Albania	1999	523

11) Merged Data

Finally we are able to merge our different variables into a merged data set. The countries and years corresponding to each observation are taken as primary key for this data set.

Country	Year	AgriLand	Change agricultural land	Change agricultural land %	Agricultural raw materials exports (% of merchandise exports)	Food exports (% of merchandise exports)	ForestArea	Deforestation	Deforestation %	Land Area in sq km	PopulationGrowth	Gross Production Value Soybeans	Export/Import of GDP	Production (quad Btu)	Coal (quad Btu)	Natural gas (quad Btu)	Petroleum and other liquids (quad Btu)	Nuclear, renewables, and other (quad Btu)	Nuclear (quad Btu)	Renewables and other (quad Btu)
Albania	1996	11310	40	0.354	9.03	11.09	7771	-19.5	-0.003	27400	-0.622	131	48.7	0.08269987229832255	0.0010542168686930385	0.0011060658	0.021922129629629517	0.05861746		0.05861746
Albania	1997	11350	40	0.352	14.06	11.11	7752	-19.5	-0.003	27400	-0.625	76	50.0	0.07303457152971403	0.0003638447599914026	0.0007373772	0.02109303556972262	0.050840314		0.050840314
Albania	1998	11390	60	0.527	8.76	9.82	7732	-19.5	-0.003	27400	-0.629	49	52.6	0.06574110056542558	0.00045713828819432637	0.0011060658	0.014498112477231255	0.049679784		0.049679784
Albania	1999	11450	-10	-0.087	4.62	5.53	7712	-19.5	-0.003	27400	-0.633	523	55.5	0.06751718849892084	0.00045713828819432637	0.0007373772	0.012830467010726508	0.053492206		0.053492206
Albania	2000	11440	-50	-0.437	5.97	6.63	7693	12.8	0.002	27400	-0.637	292	63.5	0.06083183808831241	0.00027988058460877126	0.0011060658	0.01305174370370364	0.046394148		0.046394148
Albania	2001	11390	10	0.088	5.54	5.79	7706	12.8	0.002	27400	-0.938	389	66.5	0.05136107356866748	0.00019591640922613987	0.0011060658	0.013697264359441338	0.036361827		0.036361827
Albania	2002	11400	-190	-1.667	6.52	3.56	7719	12.8	0.002	27400	-0.300	200	68.5	0.051256018118918355	0.00013994029230438563	0.0011060658	0.014638491026613971	0.035371521		0.035371521

Exploratory data analysis

To get an overview of the variables and their distribution in this table the mean and the median for each of the variable is shown. Additionally we can also see the amount of observations which are not NA values and the proportion of the NA values per variable.

Summary Statistics

Variable	NotNA	Mean	Median	PropNA
agriland	1441	486836	142936	0
chagri	1441	-216.899	0	0
chagrip	1441	0.041	0	0
agriexp	1441	4.05	1.949	0
foodexp	1441	22.637	13.565	0
forest	1441	373573.327	88962.82	0
deforest	1441	-593.54	-31.986	0
deforestp	1441	-0.001	0	0
land	1441	1151848.046	298170	0
pop	1441	1.171	1.19	0
soy	1441	705496.72	8039	0
opentr	1441	67.545	60.192	0
prod	1441	3.515	0.515	0
coal	1441	1.631	0.004	0
gas	1441	0.431	0.012	0
petrol	1441	0.8	0.033	0
nuclrew	1441	0.653	0.101	0
nuc	461	0.665	0.153	0.68
rew	1441	0.44	0.073	0

If we look at the mean and the median it tells us something about the distribution of the variable.In a perfectly symmetrical distribution, the mean and the median are the same. If the mean is lower than the median the distribution of data is skewed to the left and if the mean is higher than the median the distribution of the data is skewed to the right.

Because we did not have data for all the countries of the word this are the countries we include in our survey:

Albania	China	Kazakhstan	Rwanda
Angola	Colombia	Madagascar	Serbia
Argentina	Croatia	Malawi	Slovenia
Australia	Ecuador	Mali	South Africa
Austria	El Salvador	Mexico	Spain
Azerbaijan	Ethiopia	Morocco	Sri Lanka
Bangladesh	France	Nepal	Suriname
Belize	Georgia	Nicaragua	Switzerland
Bhutan	Germany	Nigeria	Tajikistan
Bosnia and Herzegovina	Greece	North Macedonia	Thailand
Brazil	Guatemala	Pakistan	Togo
Bulgaria	Honduras	Panama	Turkey
Burkina Faso	Hungary	Paraguay	Uganda
Burundi	India	Peru	Ukraine
Cambodia	Indonesia	Philippines	Uruguay
Cameroon	Italy	Poland	Zambia
Canada	Japan	Romania	Zimbabwe

Deforestation per country

Now lets have a look at the most important variable in our dataset which shows the amount of deforestation in sq km. First we show the affected countries in the year 2017:

Country	Year	deforest	deforestp
Brazil	2017	-10402.000	-0.002
Indonesia	2017	-6055.300	-0.006
Angola	2017	-5550.600	-0.008
Paraguay	2017	-2793.400	-0.016
Colombia	2017	-1992.900	-0.003
Zambia	2017	-1882.100	-0.004
Nigeria	2017	-1633.000	-0.007
Peru	2017	-1587.400	-0.002
Cambodia	2017	-1556.900	-0.018
Mexico	2017	-1277.700	-0.002
Argentina	2017	-1080.000	-0.004
Nicaragua	2017	-1000.000	-0.027
Ethiopia	2017	-730.000	-0.004
Ecuador	2017	-642.700	-0.005
Cameroon	2017	-560.000	-0.003
Burkina Faso	2017	-500.000	-0.008
Zimbabwe	2017	-460.700	-0.003
Malawi	2017	-420.000	-0.018
Pakistan	2017	-413.400	-0.011
Canada	2017	-369.800	0.000
South Africa	2017	-364.000	-0.002
Thailand	2017	-360.000	-0.002
Honduras	2017	-209.500	-0.003
Madagascar	2017	-132.200	-0.001
Australia	2017	-123.000	0.000
Suriname	2017	-122.400	-0.001
Panama	2017	-114.100	-0.003
Belize	2017	-111.500	-0.009
El Salvador	2017	-45.000	-0.008
Sri Lanka	2017	-31.600	-0.001
Togo	2017	-29.600	-0.002
Slovenia	2017	-20.300	-0.002
Hungary	2017	-13.500	-0.001
Albania	2017	-0.025	0.000
Bosnia and Herzegovina	2017	0.000	0.000
Burundi	2017	0.000	0.000
Georgia	2017	0.000	0.000
Germany	2017	0.000	0.000
Greece	2017	0.000	0.000
Japan	2017	0.000	0.000
Mali	2017	0.000	0.000
Nepal	2017	0.000	0.000
North Macedonia	2017	0.000	0.000
Romania	2017	0.000	0.000
Rwanda	2017	10.000	0.004
Croatia	2017	25.016	0.001
Switzerland	2017	34.400	0.003
Austria	2017	35.900	0.001
Spain	2017	42.900	0.000
Ukraine	2017	70.000	0.001
Morocco	2017	100.000	0.002
Azerbaijan	2017	115.301	0.011
Poland	2017	120.000	0.001
Bulgaria	2017	130.000	0.003
Uruguay	2017	210.000	0.011
Kazakhstan	2017	292.412	0.009
Philippines	2017	348.800	0.005
Italy	2017	538.100	0.006
France	2017	834.000	0.005
Turkey	2017	1559.000	0.007
India	2017	2664.000	0.004
China	2017	18795.700	0.009

In 2017 the countries Brazil, Indonesia, Angola, Paraguay, Colombia, Zambia, Nigeria, Peru and Cambodia are most affected by deforestation while the forest area in China and India enlarged.
3 years before this was the amount of deforestation per country:

Country	Year	deforest	deforestp
Brazil	2015	-18027.000	-0.004
Angola	2015	-5550.700	-0.008
Zambia	2015	-1883.000	-0.004
Peru	2015	-1842.500	-0.003
Colombia	2015	-1751.700	-0.003
Paraguay	2015	-1658.100	-0.009
Nigeria	2015	-1633.021	-0.007
Cambodia	2015	-1556.900	-0.018
Mexico	2015	-1277.600	-0.002
Nicaragua	2015	-1000.000	-0.026
Argentina	2015	-870.000	-0.003
Ethiopia	2015	-730.000	-0.004
Pakistan	2015	-643.600	-0.016
Ecuador	2015	-642.700	-0.005
Cameroon	2015	-560.000	-0.003
Burkina Faso	2015	-500.000	-0.008
Zimbabwe	2015	-460.700	-0.003
Thailand	2015	-440.000	-0.002
Malawi	2015	-420.000	-0.017
Canada	2015	-396.700	0.000
South Africa	2015	-364.000	-0.002
Honduras	2015	-207.800	-0.003
Madagascar	2015	-132.100	-0.001
Panama	2015	-114.100	-0.003
Suriname	2015	-113.500	-0.001
Belize	2015	-111.500	-0.008
El Salvador	2015	-45.000	-0.007
Japan	2015	-40.000	0.000
Sri Lanka	2015	-31.600	-0.001
Hungary	2015	-20.900	-0.001
Slovenia	2015	-20.300	-0.002
Albania	2015	-0.075	0.000
Greece	2015	-0.030	0.000
Bangladesh	2015	0.000	0.000
Burundi	2015	0.000	0.000
Georgia	2015	0.000	0.000
Germany	2015	0.000	0.000
Nepal	2015	0.000	0.000
Turkey	2015	0.000	0.000
Rwanda	2015	20.000	0.007
Croatia	2015	21.202	0.001
Switzerland	2015	34.400	0.003
Austria	2015	36.000	0.001
Spain	2015	40.600	0.000
Ukraine	2015	70.000	0.001
North Macedonia	2015	72.700	0.007
Bulgaria	2015	80.000	0.002
Azerbaijan	2015	95.882	0.009
Morocco	2015	151.000	0.003
Bosnia and Herzegovina	2015	160.100	0.007
Uruguay	2015	270.000	0.014
Romania	2015	280.900	0.004
Kazakhstan	2015	292.538	0.009
Philippines	2015	348.900	0.005
Italy	2015	538.100	0.006
France	2015	834.000	0.005
Indonesia	2015	2439.000	0.003
India	2015	2664.000	0.004
Australia	2015	9427.000	0.007
China	2015	21656.170	0.010

Already in 2015 Brazil Angola Zambia, Peru, Cambodia, Paraguay, Colombia, Nigeria are most affected by deforestation and the forest area in China and India enlarged. And in 2015 also the forest area of Australia and Indonesia enlarged.
If we again go 5 years more in the past we can see the following countries affected by deforestation:

Country	Year	deforest	deforestp
Brazil	2010	-15391.80	-0.003
Indonesia	2010	-9262.60	-0.009
Angola	2010	-5550.62	-0.008
Paraguay	2010	-4142.54	-0.021
Cambodia	2010	-3484.82	-0.033
Argentina	2010	-2234.00	-0.007
Zambia	2010	-1881.80	-0.004
Peru	2010	-1710.54	-0.002
Nigeria	2010	-1633.06	-0.007
Colombia	2010	-1346.26	-0.002
Mexico	2010	-1224.80	-0.002
Ethiopia	2010	-730.00	-0.004
Nicaragua	2010	-561.24	-0.013
Cameroon	2010	-560.00	-0.003
Burkina Faso	2010	-500.20	-0.007
Zimbabwe	2010	-460.70	-0.003
Malawi	2010	-420.00	-0.016
Ecuador	2010	-418.02	-0.003
Canada	2010	-413.00	0.000
South Africa	2010	-364.00	-0.002
Pakistan	2010	-322.26	-0.008
Honduras	2010	-223.28	-0.003
Madagascar	2010	-132.18	-0.001
Belize	2010	-117.12	-0.008
Panama	2010	-114.16	-0.003
Suriname	2010	-96.38	-0.001
El Salvador	2010	-45.00	-0.007
Japan	2010	-44.00	0.000
Thailand	2010	-24.00	0.000
Bangladesh	2010	-9.88	-0.001
Georgia	2010	0.00	0.000
Greece	2010	0.00	0.000
Mali	2010	0.00	0.000
Nepal	2010	0.00	0.000
Slovenia	2010	2.00	0.000
Croatia	2010	4.00	0.000
Rwanda	2010	10.00	0.004
Albania	2010	14.23	0.002
Bhutan	2010	19.78	0.001
Germany	2010	20.00	0.000
Morocco	2010	20.24	0.000
Hungary	2010	28.86	0.001
Switzerland	2010	34.38	0.003
Austria	2010	35.98	0.001
Sri Lanka	2010	50.40	0.002
North Macedonia	2010	67.94	0.007
Azerbaijan	2010	90.78	0.009
Bosnia and Herzegovina	2010	115.68	0.006
Burundi	2010	171.40	0.088
Poland	2010	182.00	0.002
Bulgaria	2010	192.00	0.005
Ukraine	2010	218.00	0.002
Philippines	2010	348.86	0.005
Uruguay	2010	377.40	0.022
Kazakhstan	2010	452.55	0.015
Italy	2010	538.08	0.006
Romania	2010	771.92	0.012
France	2010	834.00	0.005
Turkey	2010	1094.44	0.005
India	2010	2664.00	0.004
Australia	2010	7096.80	0.005
China	2010	19367.74	0.010

And also in 2010 Brazil, Angola, Paraguay, Cambodia, Zambia, Peru, Nigeria and Colombia are most affected by deforestation and the forest area of India, China and Australia enlarged. In 2010 also Indonesia is affected and Argentina is affected more than the years before by deforestation.
Having a look at the year 2005 that the map of deforestation:

and the table for 2005:

Country	Year	deforest	deforestp
Brazil	2005	-39507.90	-0.007
Paraguay	2005	-3421.44	-0.016
Argentina	2005	-3164.00	-0.010
Australia	2005	-2268.00	-0.002
Colombia	2005	-1927.71	-0.003
Indonesia	2005	-1620.80	-0.002
Mexico	2005	-1438.04	-0.002
Peru	2005	-1248.03	-0.002
Nicaragua	2005	-1211.17	-0.025
Ethiopia	2005	-730.00	-0.004
Ecuador	2005	-702.31	-0.005
Cameroon	2005	-697.01	-0.003
Burkina Faso	2005	-500.00	-0.007
Canada	2005	-479.76	0.000
Philippines	2005	-469.54	-0.007
Madagascar	2005	-468.69	-0.004
Malawi	2005	-420.00	-0.015
Pakistan	2005	-417.53	-0.010
South Africa	2005	-364.00	-0.002
Zambia	2005	-358.00	-0.001
Honduras	2005	-202.80	-0.003
Cambodia	2005	-191.77	-0.002
Panama	2005	-114.15	-0.003
Kazakhstan	2005	-74.75	-0.002
Belize	2005	-67.91	-0.005
Sri Lanka	2005	-62.80	-0.003
El Salvador	2005	-45.00	-0.007
Suriname	2005	-41.01	0.000
Bangladesh	2005	-31.99	-0.002
Rwanda	2005	-22.00	-0.008
Bosnia and Herzegovina	2005	-8.99	0.000
Burundi	2005	0.00	0.000
Mali	2005	0.00	0.000
North Macedonia	2005	2.88	0.000
Albania	2005	12.77	0.002
Slovenia	2005	14.00	0.001
Austria	2005	25.06	0.001
Croatia	2005	35.00	0.002
Ukraine	2005	38.00	0.000
Switzerland	2005	38.54	0.003
Azerbaijan	2005	45.27	0.004
Georgia	2005	61.80	0.002
Japan	2005	90.00	0.000
Bhutan	2005	99.29	0.004
Hungary	2005	125.22	0.006
Romania	2005	149.00	0.002
Morocco	2005	168.03	0.003
Poland	2005	270.00	0.003
Greece	2005	301.57	0.008
Bulgaria	2005	362.00	0.010
Uruguay	2005	362.30	0.023
Italy	2005	658.79	0.008
Turkey	2005	934.73	0.005
Thailand	2005	1075.00	0.006
France	2005	1131.00	0.007
Spain	2005	1451.41	0.008
India	2005	1905.00	0.003
China	2005	23609.83	0.013

Also in 2005 year the most affected countries are Brazil, Paraguay, Argentina, Colombia and Indonesia and the forest aria in China and India already anlarged. In 2005 also Australia was affected by deforestation.
This last map shows the deforestation in sq km in the year 2001:

Country	Year	deforest	deforestp
Brazil	2001	-39507.90	-0.007
Paraguay	2001	-3421.44	-0.015
Argentina	2001	-3164.00	-0.010
Australia	2001	-2268.00	-0.002
Colombia	2001	-1927.71	-0.003
Nigeria	2001	-1633.06	-0.007
Indonesia	2001	-1620.80	-0.002
Mexico	2001	-1438.04	-0.002
Peru	2001	-1248.03	-0.002
Nicaragua	2001	-1211.17	-0.023
Ethiopia	2001	-730.00	-0.004
Ecuador	2001	-702.31	-0.005
Cameroon	2001	-697.01	-0.003
Burkina Faso	2001	-500.00	-0.007
Canada	2001	-479.76	0.000
Philippines	2001	-469.54	-0.006
Madagascar	2001	-468.69	-0.004
Pakistan	2001	-417.53	-0.009
South Africa	2001	-364.00	-0.002
Honduras	2001	-202.80	-0.003
Cambodia	2001	-191.77	-0.002
Panama	2001	-114.15	-0.003
Kazakhstan	2001	-74.75	-0.002
Belize	2001	-67.91	-0.005
Sri Lanka	2001	-62.80	-0.003
El Salvador	2001	-45.00	-0.007
Suriname	2001	-41.01	0.000
Rwanda	2001	-22.00	-0.008
Burundi	2001	0.00	0.000
Mali	2001	0.00	0.000
North Macedonia	2001	2.88	0.000
Albania	2001	12.77	0.002
Slovenia	2001	14.00	0.001
Austria	2001	25.06	0.001
Croatia	2001	35.00	0.002
Ukraine	2001	38.00	0.000
Switzerland	2001	38.54	0.003
Azerbaijan	2001	45.27	0.005
Germany	2001	55.00	0.000
Georgia	2001	61.80	0.002
Japan	2001	90.00	0.000
Hungary	2001	125.22	0.006
Romania	2001	149.00	0.002
Morocco	2001	168.03	0.003
Greece	2001	301.57	0.008
Bulgaria	2001	362.00	0.011
Uruguay	2001	362.30	0.026
Italy	2001	658.79	0.008
Turkey	2001	934.73	0.005
Thailand	2001	1075.00	0.006
France	2001	1131.00	0.007
Spain	2001	1451.41	0.008
India	2001	1905.00	0.003
China	2001	23609.83	0.013

And already in 2001 Brazil, Paraguay, Argentina, Colombia, Nigeria and Indonesia were the countries which are most affected by deforestation and the forest area in China and India enlarged.

Now we have identified th most interesting countries regarding the amount of yearly deforestation and have a deeper look at them.

The yearly deforestation in Brazil has the highest amount of the world, also if in 2010 the amount decreases to ca. 15000 sq km and in 2017 the yearly amount also decreases to 10402 sq km.

The yearly amount of deforestation in Indonesia increased in 2010 from 1620 sq km to 9262 sq km. In the year 2015 the forest area enlarged by 2439 sq km, but one year later the country was already affected by deforestation of 13220 sq km.

In Angola the yearly amount of deforestation was around 5550 from 2010 to 2017. For the years before 2010 we do not have any data in our dataset.

The yearly deforestation amount in Paraguay increased in 2010 from 3421 to 4142 sq km. In 2015 the amount of deforestation was just 1658 but one year later it was already very high with 3928 sq km.

In Colombia the amount of yearly deforestation decreased in 2010 from around 2000 sq km to 1300 sq km an increased in 2016 again to above 2000 sq km.

In Zambia we can see a increase of the yearly amount of deforestation between the year 2009 and 2010 from around 300 to 1800 sq km.

In Nigeria the yearly amount of deforestation was nearly the same in the years from 2001 to 2017.

In Peru the yearly amount of deforestation in the years 2001 to 2009 was around 1200 and from 2010 to 2017 the amount was between 1500 and 1800 sq km.

In Cambodia the yearly amount deforestation decreased between 2009 and 2010 from under 200 sq km to above 3000 sq km and decreased in the year 2015 to around 1500 sq km.

In Argentina the yearly amount of deforestation decreased in 2009 from around 3000 to around 2000 sq km and in 2015 to around 1000 sq km.

Australia was from 2001 to 2009 affected by deforestation of above 2000 sq km each year, from the years 2010 to 2015 the forest area was enlarged but in the years 2016 and 2017 the country was again affected by deforestation even if just by a low extend.
Now we again visualized the distribution of the deforestation variable with a boxplot: We can see that the interquartile range is very small, that is because we have a lot of far away outliers. We also already know which countries most of this this outliers are. The lowest values for example are China, India and in 2010 and 2015 also Australia while the highest values in for example are Brazil, Paraguay and Angola. At first we were sceptical about the high increase of China and India but after a research we came across a paper that deals with the subject of the leading role of China and India in the greening of the world.(Chen, 2019) To put at the end of the report: Chen et al. (2019) China and India lead in greening of the world through land-use management. Nature Sustainability, (2) 122–129. In our analysis we are just interested in the absolute amount of deforestation therefore we do not take the percentage of deforestation into account. We know that using the absolute values does not help to exam the deforestation without the dependence to the amount of forest area of the country but we think that the amount of forest area also plays an important role in real word decisions regarding deforestation.

The other problem is that in our variable of interest (deforest) we have two opposite effect included. One is the deforestation and the other is the fact that some countries adopted a Green policy and planted trees in order to reforest their country. Our goal with this regression analysis is to identify the factors of the deforestation. As both effects are included in the same variable (deforest) it will be hard to differentiate them. However we could filter to take into account only the countries that had a positive value of deforestation and set the others equal to zero in order to not lose the total observation.

After this data transformation our boxplot looks like this: Now we do not have any outliers below the box plot, the outliers above we will keep because this are the observations which are the most interesting ones.

Distribution of aricultural land

Also the variable agricultural land has a lot of outiers, and in all years the median is lower than the mean because the box plots are located at the bottom of the plot. Between the years we can not see a important change of the agricultural land. Most of the values are between 0 and 100000 sq km. Now we will have a look at some outlies with the most difference to the rest of the samples in each year to better understand and proof the values of the data.

Outliers 1995

Country	agriland
China	5237140
Australia	4633880
Brazil	2278050
Kazakhstan	2171865
India	1809450
Argentina	1280450
Mexico	1061950
South Africa	975200
Canada	612610
Colombia	445130
Indonesia	429780

Outliers 2000

Country	agriland
China	5237310
Australia	4554690
Brazil	2283235
Kazakhstan	2153933
India	1809750
Argentina	1285100
Mexico	1063300
South Africa	981250
Nigeria	661840
Canada	612870
Indonesia	471770
Colombia	448590

Outliers 2005

Country	agriland
China	5266822
Australia	4102300
Brazil	2288420
Kazakhstan	2122860
India	1801260
Argentina	1377975
Mexico	1065700
South Africa	974830
Canada	616560
Indonesia	518460
Colombia	425570

Outliers 2010

Country	agriland
China	5289168
Australia	3763720
Brazil	2318342
Kazakhstan	2171618
India	1795730
Argentina	1474810
Mexico	1028440
South Africa	968910
Nigeria	678170
Canada	582800
Indonesia	556000
Angola	525120
Colombia	425030

Outliers 2015

Country	agriland
China	5286334
Australia	3481190
Brazil	2354382
Kazakhstan	2162597
India	1796740
Argentina	1487000
Mexico	1032120
South Africa	963410
Nigeria	686336
Canada	579850
Indonesia	573000
Angola	552873
Colombia	447539

In this tables we see that the outliers are mostly the same and most of this countries we already know well from the exploration of the amount of deforestation. China and India have a high amount of agricultural land and in the same years the forest area of this countries enlarged. The countries Colombia, Angola, Nigeria, Argentina, Brazil and Indonesia also have a high amount of agricultural land and were the countries most affected by deforestation. Also Australia has a high amount of agricultural land and was eigher affected by deforestation or enlarged its forest area.

Distibution of agricultural raw material export

Also the variable agricultural raw material exports has some outiers, and in all years the median is lower than the mean because the box plots are located at the bottom of the plot. Between the years we can not see a important change of the agricultural land. Most of the values are between 0 and 10 %. Now we will have a look at some outlies with the most difference to the rest of the samples in each year to better understand and proof the values of the data.

Outliers 1995

Country	agriexp
Burkina Faso	62.7
Paraguay	36.4
Cameroon	27.5
Uruguay	14.9
Ethiopia	13.4

Outliers 2000

Country	agriexp
Burkina Faso	58.0
Mali	36.1
Ethiopia	17.6
Tajikistan	12.2

Outliers 2005

Country	agriexp
Burkina Faso	74.7
Mali	24.5
Cameroon	19.3
Albania	13.9
Ethiopia	12.9

Outliers 2010

Country	agriexp
Burkina Faso	17.5
Cameroon	14.8
Uruguay	10.5

Outliers 2015

Country	agriexp
Cameroon	16.9
Burkina Faso	13.7
Uruguay	13.5

In this tables we can see that the outliers are mostly the same like Burkina Faso, Cameroon, Uruguay, Ethiopia and Mali.

Distribution of poplutatio growth

In this plot we can see that that the median and the mean of this variable is mostly the same because the boxplot is located in the middle of the plot. Between 1995 and 2000 the median of the popluation growth decreased by cs. 0,25 % and the population growth of all the countries is between -2 and 3,5 %. For this variable we do not have any outliers.

Distribution of soy production

The variable soy production has a lot of outiers, and in all years the median is lower than the mean because the box plots are located at the bottom of the plot. Between the years we can also see that the values of the outliers are growing. Now we will have a look at some outlies with the most difference to the rest of the samples in each year to better understand and proof the values of the data.

Outliers 1995

Country	soy
Brazil	4533712
China	3335324
Argentina	2633388
India	1413272
Indonesia	845549
Canada	483808

Outliers 2000

Country	soy
Brazil	5131057
China	4429960
Argentina	3626257
India	1015444
Paraguay	555606
Japan	523359

Outliers 2005

Country	soy
Brazil	10180069
China	6563619
Argentina	6527335
India	1894838
Canada	716117

Outliers 2010

Country	soy
Brazil	24751814
Argentina	13838818
China	11139314
India	5035321
Paraguay	2528581
Canada	1656694
Uruguay	681203
Indonesia	669781

Outliers 2015

Country	soy
Brazil	30907303
Argentina	12940965
China	9524513
India	3141986
Paraguay	2855165
Canada	2128292
Ukraine	1341227
Uruguay	1040094
Indonesia	599012

In most of the years the outliers are Brazil, China, Argentina, India, Canada, Paraguay, Indonesia and Uruguay, because these countries produced most soy.

Distribution of openess to trade

In this plot we can see that the median of tha ratio of imports and exports in percent of the GDP is growing from 1995 to 2005. Than countries ratios of imports and exports are spread bewtween 25 and 150 % of GDP. Now we will have a look on the countries with the 10 highest values in each year to get an impression which countries are the most open to trade.

Countries that are most opened to trade in 1995

Country	opentr
Panama	175.5
Suriname	140.8
Paraguay	130.7
Honduras	114.9
Belize	105.6
Slovenia	93.5
Thailand	89.8
Kazakhstan	82.5
Hungary	78.3
Switzerland	77.1

Countries that are most opened to trade in 2000

Country	opentr
Tajikistan	193
Panama	140
Hungary	137
Belize	127
Thailand	121
Honduras	120
Ukraine	116
Cambodia	112
Suriname	109
Kazakhstan	106

Countries that are most opened to trade in 2005

Country	opentr
Panama	142
Thailand	138
Cambodia	137
Honduras	136
Hungary	127
Slovenia	120
Belize	117
Azerbaijan	116
Suriname	111
Paraguay	104

Countries that are most opened to trade in 2010

Country	opentr
Hungary	158
Panama	148
Thailand	127
Slovenia	127
Switzerland	117
Belize	116
Cambodia	114
Bhutan	113
Honduras	109
Paraguay	107

Countries that are most opened to trade in 2015

Country	opentr
Hungary	170
Slovenia	145
Bulgaria	128
Cambodia	128
Thailand	126
Belize	124
North Macedonia	114
Switzerland	113
Ukraine	108
Honduras	107

We can see that Panama, Suriname, Honduras, Belize, Thailand, Kazakhstan, Hungary, Cambodia, Honduras, Slovenia, Paraguay, Switzerland and Ukraine are in many years under 10 most opended to trade countries.

Distribution of coal production

The variable coal production has a lot of outiers, and in all years the median is lower than the mean because the box plots are located at the bottom of the plot. Between the years we can also see that the values of the outliers are growing and that most of the values are near to 0 quad. Now we will have a look at some outlies with the most difference to the rest of the samples in each year to better understand and proof the values of the data.

Outliers in 1995

Country	coal
China	28.779
Australia	5.262
India	4.695
South Africa	4.389
Germany	3.418
Kazakhstan	2.146
Canada	1.587
Indonesia	0.879
Colombia	0.655
Spain	0.503

Outliers in 2000

Country	coal
China	30.828
Australia	6.533
India	5.396
South Africa	4.929
Germany	2.579
Kazakhstan	1.990
Indonesia	1.783
Ukraine	1.545
Canada	1.464
Colombia	0.966

Outliers in 2005

Country	coal
China	52.67
Australia	7.99
India	7.03
South Africa	5.37
Indonesia	3.79
Poland	2.72
Kazakhstan	2.20
Colombia	1.50
Ukraine	1.50
Canada	1.39

Outliers in 2010

Country	coal
China	76.36
Australia	9.17
India	8.62
Indonesia	5.74
South Africa	5.58
Kazakhstan	2.95
Poland	2.24
Germany	1.93
Colombia	1.88
Canada	1.44

Outliers in 2015

Country	coal
China	85.452
Australia	11.562
India	9.587
Indonesia	9.357
South Africa	5.548
Kazakhstan	2.908
Colombia	2.226
Germany	1.796
Canada	1.269
Ukraine	0.831

We can see that the outlierwith the highes production value is every year China. Australia, India, South Africa, Germany, Kazakhstan, Indonesia, Canada, Colombia, Ukraine, Poland are in most of the years outliers with the most difference to the other values.

Distribution of gas production

The variable gas production has a lot of outiers, and in all years the median is lower than the mean because the box plots are located at the bottom of the plot. Most of the values are below 1 quad. Now we will have a look at some outlies with the most difference to the rest of the samples in each year to better understand and proof the values of the data.

Outliers in 1995

Country	gas
Canada	5.72
Indonesia	2.44

Outliers in 2000

Country	gas
Canada	6.60
Indonesia	2.44
Argentina	1.38
Mexico	1.36

Outliers in 2005

Country	gas
Canada	6.738
Indonesia	2.181
China	1.842
Argentina	1.684
Mexico	1.553
Australia	1.382
India	1.092
Pakistan	0.944

Outliers in 2010

Country	gas
Canada	5.57
China	3.48
Indonesia	3.18
Australia	1.96
India	1.94
Mexico	1.85
Argentina	1.48
Thailand	1.25
Pakistan	1.21

Outliers in 2015

Country	gas
China	4.904
Australia	2.503
India	1.155
Indonesia	2.802
South Africa	0.041

We can see that in most years the outliers are the countries Canada, Indonesia, Argentina, Mexico, Australia, India and Pakistan.

Distribution of petrol production

The variable petrol production has a lot of outiers, and in all years the median is lower than the mean because the box plots are located at the bottom of the plot. Most of the values are below 2 quad. Now we will have a look at some outlies with the most difference to the rest of the samples in each year to better understand and proof the values of the data.

Outliers in 1995

Country	petrol
China	6.42
Mexico	6.41
Canada	4.67
Indonesia	3.26
Argentina	1.62

Outliers in 2000

Country	petrol
Mexico	7.29
China	6.99
Canada	5.22
Nigeria	4.66
Indonesia	3.13
Brazil	2.80
Argentina	1.74
Australia	1.58
Kazakhstan	1.56

Outliers in 2005

Country	petrol
Mexico	7.95
China	7.75
Canada	5.96
Brazil	3.65
Kazakhstan	2.83
Indonesia	2.26
Argentina	1.72
India	1.58
Ecuador	1.17
Colombia	1.16
Australia	1.07

Outliers in 2010

Country	petrol
China	8.76
Canada	6.68
Mexico	6.19
Nigeria	5.25
Brazil	4.56
Angola	4.08
Kazakhstan	3.40
Azerbaijan	2.23
Indonesia	2.05
India	1.76
Colombia	1.73
Argentina	1.51
Australia	1.08

Outliers in 2015

Country	petrol
China	9.19
Canada	8.82
Mexico	5.43
Brazil	5.40
Nigeria	4.73
Angola	3.87
Kazakhstan	3.69
Colombia	2.21
Azerbaijan	1.83
India	1.77
Indonesia	1.71

The countries China, Mexico, Canada, Indonesia, Argentina, Brazil, Kazakhstan, Australia, Nigeria, India, Colombia, Angola and Azerbaijan are the outliers in most of the years.

Distibution of nuclear production

The variable nuclear production has not that much outiers than the other variables, and in all years the median is lower than the mean because the box plots are located at the bottom of the plot. Most of the values are below 2 quad. Now we will have a look at the few outlies in each year to better understand and proof the values of the data.

Outliers in 1995

Country	nuc
France	3.71
Japan	2.83

Outliers in 2000

Country	nuc
France	4.08
Japan	3.11

Outliers in 2005

Country	nuc
France	4.46
Japan	2.85

Outliers in 2010

Country	nuc
France	4.26
Japan	2.85

Outliers in 2015

Country	nuc
France	4.35

` The two outlier we can see in all of the years are France and Japan.

Distribution of production of renewables ant others

The variable renewables production again has some outliers, and in all years the median is lower than the mean because the box plots are located at the bottom of the plot. Most of the values are below 1 quad. Now we will have a look at the outlies in each year to better understand and proof the values of the data.

Outliers in 1995

Country	rew
Canada	3.49
Brazil	2.94
China	1.94
Japan	1.00

Outliers in 2000

Country	rew
Canada	3.71
Brazil	3.39
China	2.28
Japan	1.28

Outliers in 2005

Country	rew
China	4.01
Brazil	3.84
Canada	3.69
Japan	1.25
India	1.16

Outliers in 2010

Country	rew
China	7.720
Brazil	4.853
Canada	3.635
India	1.450
Japan	1.392
Germany	1.229
France	0.876
Italy	0.802

Outliers in 2015

Country	rew
China	13.118
Brazil	4.727
Canada	3.938
Germany	1.952
India	1.818
Japan	1.710
Italy	1.057
France	0.989

Canada, Brazil, China, Japan, India, Germany, Italy and France are the outliers in most of the years.

Correlation between the different variables

We want to show which factors have influence on the amount of deforestation, therefore we have a look on the correlation coefficients that show if and which correlation is between the deforestation and the other variables we have in our data set. The correlation coefficient of all the variables to each other we can see in this correlation plot.

We are especially interested in the correlation coefficient between the variable deforestation and all the other variables,which we can see in this table:

	deforest
Country	-0.17
Year	-0.08
agriland	0.25
chagri	0.01
chagrip	0.03
agriexp	0.01
foodexp	0.05
forest	0.74
deforest	1.00
deforestp	-0.19
land	0.43
pop	0.07
soy	0.46
opentr	-0.21
prod	0.06
coal	-0.03
gas	0.07
petrol	0.23
nuclrew	0.22
nuc	-0.15
rew	0.32

It may be that not all of these correlation coefficients are significant, therefore we show an additional correlation plot that gives us the information if there is a significant correlation between the variables.

deforest	corr	sign
agriexp	0.01	0.609
agriland	0.25	0.000
chagri	0.01	0.655
chagrip	0.03	0.252
coal	-0.03	0.290
Country	-0.17	0.000
deforest	1.00	0.000
deforestp	-0.19	0.000
foodexp	0.05	0.082
forest	0.74	0.000
gas	0.07	0.007
land	0.43	0.000
nuc	-0.15	0.002
nuclrew	0.22	0.000
opentr	-0.21	0.000
petrol	0.23	0.000
pop	0.07	0.005
prod	0.06	0.031
rew	0.32	0.000
soy	0.46	0.000
Year	-0.08	0.004

Like we can see in this table there is a significant positive correlation between deforestation and agricultural land, gas production, petrol production, population growth, production of reweables and soy production and a negative significant correlation between nuclear production and openess to trade. The significant correlation between deforestation and country is not of interest for us because the variable country is just encoded by alphabetic order and therefore does not give us a lot of information. The negative correlation between deforestation and deforestation in percent comes from the transformation of of the deforesation variable from negative to positive values and is for our porpuse not of interest. Of course we are also interested in the correlation coefficients between other variables than deforrstation to be aware of multicollinearity problems. This we will cover in the analysis part, because like we can see in the correlation plot there is a significant correlation between agricultural land and soy production, gas production, openess to trade, petrol production, nuclear production and production of reweables. This is also the case for more variables.

Correlation between deforestation and agricultural land

In this plot we can see a light tendency from the left bottom to right above, this shows the low positive correlation between agriccultural land and deforestation.

Correlation between deforestation and gas production

In this plot we can see a light tendency from the left bottom to right above, this shows the low positive correlation between agriccultural land and deforestation also if this tendency like also the correlation is very low because we can also see that there are a lot observationsat the right bottom and in the left upper corner.

Correlation between deforestation and nuclear production

#> Warning: Removed 980 rows containing missing values (geom_point).

Between this two variables is a negativ correlation, this correlation is very low therfore it is hard tho show in a scatter plot.

Correlation between deforestation and openess to trade

In this plot we can see that there is a light tendency from the left upper corner to the right lower corner, but again the correlation is very low.

Correlation between deforestation and petrol production

In this plot we can see a light tendency from the left lower corner to right upper, this shows the low positive correlation between petrol production and deforestation.

Correlation between deforestation and population growth

Between this two variables is a positive correlation, this correlation is very low therfore it is hard tho show in a scatter plot because we can see that most of the observations are in the lower middle.

Correlation between deforestation and soy production

In this plot we can see a light tendency from the left bottom to right above, this shows the moderate positive correlation between soy production and deforestation.

Correlation between deforestation and renewables production

In this plot we can see a light tendency from the left bottom to right above, this shows the positive correlation between renewables production and deforestation.

Analysis

Structure of the analysis

1. Ordinary Least Square regression
2. Model Selection
3. Heteroskedasticity
4. Panel Data Model
5. Balanced Model
6. OLS per year
7. Comparison of the results
8. Answer to the research question

1. OLS regression

We can now finally try to run the regression with our initial model. We will first try to run a basic OLS regression with deforestation as our dependent variable; our explanatory variables being : * the agricultural land (agriland) * the agricultural export (agriexp) * the food exportation (foodexp) * the population growth (pop) * the soy production (soy) * the openness to trade (opentr) * and all the extraction of minerals variables: ** total mineral production (prod) ** the coal production (coal) ** the gas production (gas) ** the petrol production (petrol) ** the nuclear production (nuc) ** the renewable production (rew)

All our variables are measured by country and each year. First we will use the “merge” data set. In this sample, the years are going from 1980 to 2017

So here is our first OLS regression of an initial model including almost all variables.

Intitial model

Observations	461 (980 missing obs. deleted)
Dependent variable	deforest
Type	OLS linear regression

F(11,449)	102.29
R²	0.71
Adj. R²	0.71

	Est.	S.E.	t val.	p
(Intercept)	-4690.10	948.69	-4.94	0.00
agriland	0.00	0.00	7.64	0.00
agriexp	843.89	175.09	4.82	0.00
foodexp	149.14	23.70	6.29	0.00
pop	397.68	274.20	1.45	0.15
soy	-0.00	0.00	-7.33	0.00
opentr	18.64	8.24	2.26	0.02
prod	5423.76	254.35	21.32	0.00
coal	-6100.78	277.79	-21.96	0.00
gas	-9691.62	454.34	-21.33	0.00
petrol	-4869.97	313.80	-15.52	0.00
nuclrew	NA	NA	NA	NA
nuc	-6394.03	384.86	-16.61	0.00
rew	NA	NA	NA	NA
Standard errors: OLS

2. Model selection

Before proceeding with the model selection, we will comment the result of the OLS regression. Our R-squared is quite high which indicated that our model is explaining most of our model, this result is very optimistic and will prove to be unrealistic. In fact, in this first regression, the data are explaining 70.8% of our model. As we included in this first regression all our variable without any selection, it is not so surprising to have a high R-squared.

The dependent variable of our model, deforest, is now negative when the forest area increased from one year to the next and positive when the area of forest decreased, in order to get the deforestation. Now we will check at the sign of our estimators to see whether it goes in line with our expectation or not.For example, agriland coefficient is of positive sign, which is intuitively good as we picked this variable to see if the agriculture increases the deforestation. In fact they are all supposed to increase if the deforestation increases and thus, be of positive sign. We notice that the soy production and all the extraction of minerals coefficients are not of the sign expected. This is probably due to the fact that we have the total production as well as the production of each minerals separately. There is then a strong similarity of information and thus a multicolinearity issue.

Multicollinearity problem occurs when two or more variables are strongly correlated one another. The issue comes with the fact that we can therefore not isolate the effect of each variable. For the prediction it doesn’t matter much but for the measurement of influence of every factor it matters. We can see it here through a sign of the coefficients that make no sense.

In order to solve this strong multicollinearity issue we need to make a choice on which variable would be the most significant to keep and which we should remove.To measure the similarity of information we will use the VIF indicator:

#>    Variables Tolerance  VIF
#> 1   agriland     0.121 8.26
#> 2    agriexp     0.505 1.98
#> 3    foodexp     0.478 2.09
#> 4        pop     0.610 1.64
#> 5        soy     0.338 2.96
#> 6     opentr     0.468 2.14
#> 7       prod     0.000  Inf
#> 8       coal     0.000  Inf
#> 9        gas     0.000  Inf
#> 10    petrol     0.000  Inf
#> 11   nuclrew     0.000  Inf
#> 12       nuc     0.000  Inf
#> 13       rew     0.000  Inf

The VIF indicates the presence of multicollinearity if it is above 5. We will remove one by one the variables with the strongest VIF. Intuitively the variable “prod” contains all the extraction variables so it is obvious not to use them both in our model. As we want each factor isolated we will first remove prod from the initial model and re-run the VIF indicators.

Second model

Observations	461 (980 missing obs. deleted)
Dependent variable	deforest
Type	OLS linear regression

F(11,449)	102.29
R²	0.71
Adj. R²	0.71

	Est.	S.E.	t val.	p
(Intercept)	-4690.10	948.69	-4.94	0.00
agriland	0.00	0.00	7.64	0.00
agriexp	843.89	175.09	4.82	0.00
foodexp	149.14	23.70	6.29	0.00
pop	397.68	274.20	1.45	0.15
soy	-0.00	0.00	-7.33	0.00
opentr	18.64	8.24	2.26	0.02
coal	-677.02	40.47	-16.73	0.00
gas	-4267.87	299.00	-14.27	0.00
petrol	553.79	177.86	3.11	0.00
nuclrew	5423.76	254.35	21.32	0.00
nuc	-6394.03	384.86	-16.61	0.00
rew	NA	NA	NA	NA
Standard errors: OLS

#>    Variables Tolerance  VIF
#> 1   agriland     0.121 8.26
#> 2    agriexp     0.505 1.98
#> 3    foodexp     0.478 2.09
#> 4        pop     0.610 1.64
#> 5        soy     0.338 2.96
#> 6     opentr     0.468 2.14
#> 7       coal     0.134 7.47
#> 8        gas     0.203 4.93
#> 9     petrol     0.182 5.51
#> 10   nuclrew     0.000  Inf
#> 11       nuc     0.000  Inf
#> 12       rew     0.000  Inf

Now we notice “nuclrew” to have the highest VIF. Again, not surprising as it contains both “nuc” and “rew” information. We will remove it and re-run

Third model

Observations	461 (980 missing obs. deleted)
Dependent variable	deforest
Type	OLS linear regression

F(11,449)	102.29
R²	0.71
Adj. R²	0.71

	Est.	S.E.	t val.	p
(Intercept)	-4690.10	948.69	-4.94	0.00
agriland	0.00	0.00	7.64	0.00
agriexp	843.89	175.09	4.82	0.00
foodexp	149.14	23.70	6.29	0.00
pop	397.68	274.20	1.45	0.15
soy	-0.00	0.00	-7.33	0.00
opentr	18.64	8.24	2.26	0.02
coal	-677.02	40.47	-16.73	0.00
gas	-4267.87	299.00	-14.27	0.00
petrol	553.79	177.86	3.11	0.00
nuc	-970.28	229.11	-4.23	0.00
rew	5423.76	254.35	21.32	0.00
Standard errors: OLS

#>    Variables Tolerance  VIF
#> 1   agriland     0.121 8.26
#> 2    agriexp     0.505 1.98
#> 3    foodexp     0.478 2.09
#> 4        pop     0.610 1.64
#> 5        soy     0.338 2.96
#> 6     opentr     0.468 2.14
#> 7       coal     0.134 7.47
#> 8        gas     0.203 4.93
#> 9     petrol     0.182 5.51
#> 10       nuc     0.666 1.50
#> 11       rew     0.169 5.92

We now have “agriland” with the highest VIF. By checking the correlation plot we see that “agriland” is mainly correlated with some extraction variables. We will not remove agriland as it is one of the variable we are most interested in. We will then remove “coal” first as it has the second highest VIF.

Fourth model

Observations	461 (980 missing obs. deleted)
Dependent variable	deforest
Type	OLS linear regression

F(10,450)	52.19
R²	0.54
Adj. R²	0.53

	Est.	S.E.	t val.	p
(Intercept)	-3601.28	1204.57	-2.99	0.00
agriland	-0.00	0.00	-6.00	0.00
agriexp	2131.63	200.16	10.65	0.00
foodexp	244.89	29.27	8.37	0.00
pop	676.31	348.33	1.94	0.05
soy	-0.00	0.00	-2.17	0.03
opentr	-5.71	10.32	-0.55	0.58
gas	-5352.87	371.48	-14.41	0.00
petrol	1579.68	212.49	7.43	0.00
nuc	-749.05	291.11	-2.57	0.01
rew	3183.05	275.20	11.57	0.00
Standard errors: OLS

#>    Variables Tolerance  VIF
#> 1   agriland     0.282 3.54
#> 2    agriexp     0.626 1.60
#> 3    foodexp     0.508 1.97
#> 4        pop     0.613 1.63
#> 5        soy     0.367 2.72
#> 6     opentr     0.483 2.07
#> 7        gas     0.213 4.70
#> 8     petrol     0.206 4.85
#> 9        nuc     0.669 1.50
#> 10       rew     0.234 4.28

Removing the coal variable had a significant impact on the R-squared. But now all our VIF indicators are below five. We could have expect a correlation between “agriexp” and “foodexp” but the VIF of both is below 5, so we can keep both of them for the moment.

However, the “opentr” coefficient isn’t significant (large p-value) we will then try to run the regression without it:

Fifth model

Observations	461 (980 missing obs. deleted)
Dependent variable	deforest
Type	OLS linear regression

F(9,451)	58.04
R²	0.54
Adj. R²	0.53

	Est.	S.E.	t val.	p
(Intercept)	-4185.57	578.43	-7.24	0.00
agriland	-0.00	0.00	-6.01	0.00
agriexp	2151.33	196.81	10.93	0.00
foodexp	248.82	28.38	8.77	0.00
pop	777.35	296.36	2.62	0.01
soy	-0.00	0.00	-2.17	0.03
gas	-5379.95	367.96	-14.62	0.00
petrol	1585.33	212.08	7.48	0.00
nuc	-684.46	266.45	-2.57	0.01
rew	3189.29	274.76	11.61	0.00
Standard errors: OLS

#>   Variables Tolerance  VIF
#> 1  agriland     0.293 3.42
#> 2   agriexp     0.647 1.55
#> 3   foodexp     0.539 1.85
#> 4       pop     0.845 1.18
#> 5       soy     0.367 2.72
#> 6       gas     0.217 4.62
#> 7    petrol     0.207 4.84
#> 8       nuc     0.797 1.25
#> 9       rew     0.234 4.27

Removing the openness to trade didn’t lower the R-squared and the coefficients are all significant at 95% which is great.

Nevertheless, we notice that even after dealing with the multicollinearity, we still have four out of nine of our coefficients are negative (of wrong sign).

We can try to run the AIC criteria in order to see whether it identifies other variables to delete.

#> Start:  AIC=7717
#> deforest ~ agriland + agriexp + foodexp + pop + soy + opentr + 
#>     prod + coal + gas + petrol + nuclrew + nuc + rew
#> 
#> 
#> Step:  AIC=7717
#> deforest ~ agriland + agriexp + foodexp + pop + soy + opentr + 
#>     prod + coal + gas + petrol + nuclrew + nuc
#> 
#> 
#> Step:  AIC=7717
#> deforest ~ agriland + agriexp + foodexp + pop + soy + opentr + 
#>     prod + coal + gas + petrol + nuc
#> 
#>            Df  Sum of Sq         RSS  AIC
#> <none>                    8148592726 7717
#> - pop       1   38174490  8186767217 7717
#> - opentr    1   92853771  8241446497 7720
#> - agriexp   1  421570400  8570163127 7738
#> - foodexp   1  718650843  8867243570 7754
#> - soy       1  975299419  9123892145 7767
#> - agriland  1 1059159049  9207751775 7771
#> - petrol    1 4370906888 12519499614 7913
#> - nuc       1 5009293991 13157886717 7936
#> - prod      1 8252403012 16400995738 8038
#> - gas       1 8257811005 16406403731 8038
#> - coal      1 8753073376 16901666103 8051
#> 
#> Call:
#> lm(formula = deforest ~ agriland + agriexp + foodexp + pop + 
#>     soy + opentr + prod + coal + gas + petrol + nuc, data = merged)
#> 
#> Coefficients:
#>  (Intercept)      agriland       agriexp       foodexp  
#> -4690.102251      0.003445    843.893616    149.144817  
#>          pop           soy        opentr          prod  
#>   397.677584     -0.000469     18.642477   5423.756412  
#>         coal           gas        petrol           nuc  
#> -6100.775318  -9691.622319  -4869.969108  -6394.032951

We can see that it does not identify other variables to remove. The AIC criteria doesn’t take into account the multicollinearity. Therefore from multiple perspectives we end up chosing the fifth model.

3. Heteroskedasticity

Even if we solved the multicollinearity issue and that most of our variables are significant, we still have to check the distribution of our error term to determine whether our model is good or not.

We will now plot our residuals in order to see whether the fact that we have a panel data impacts or not the independence of our error term.

We clearly see a pattern in the error term indicating that it is not independently identically distributed. Our error term is then heteroskedastic. Our first intuition is to think that it comes from the fact that we are measuring a cross-sectional data through time. But in fact it can come from other sources of heteroskedasticity problem like an omitted variable bias.

To try to solve this problem we are going to use a panel data model.

4. Panel Data Model

Now we can further improve our model by taking into account the fact that our variables are observed among different countries over a period of time. Having observations taken over a time period implies a correlated error term among the different observations because of other unobserved factors that are link to the time period, and thus bias our estimators. As we have many variable in the same case we have a strong incentive to use a panel data.

There are different method to analyse panel data. The “pooling” method will basically give the same results as our previous OLS model and we found it to have problem with heteroskedasticity. We need a model that assumes that not all countries have the same intercept. In our case we are not sure whether to use the “random” or the “fixed effect” model for our panel data. Therefore, we will first run this two regression without any transformation and taking back the model with almost all the variables (third model).

Comparison fixed vs random effect

#> Oneway (individual) effect Within Model
#> 
#> Call:
#> plm(formula = deforest ~ agriland + agriexp + foodexp + pop + 
#>     soy + opentr + coal + gas + petrol + nuc + rew, data = merged, 
#>     model = "within", index = c("Country", "Year"))
#> 
#> Unbalanced Panel: n = 19, T = 5-27, N = 461
#> 
#> Residuals:
#>     Min.  1st Qu.   Median  3rd Qu.     Max. 
#> -10532.1   -259.8    -24.2    191.6  12925.1 
#> 
#> Coefficients:
#>              Estimate   Std. Error t-value             Pr(>|t|)    
#> agriland    0.0239215    0.0033714    7.10      0.0000000000053 ***
#> agriexp   -41.9811343   84.6503398   -0.50                0.620    
#> foodexp    -8.3653365   20.6163293   -0.41                0.685    
#> pop      -207.7198014  221.0946618   -0.94                0.348    
#> soy        -0.0007609    0.0000305  -24.98 < 0.0000000000000002 ***
#> opentr      9.9071633    5.0202971    1.97                0.049 *  
#> coal       35.0939705   26.5753008    1.32                0.187    
#> gas      -185.3113614  306.5952980   -0.60                0.546    
#> petrol    391.3110210  157.0977542    2.49                0.013 *  
#> nuc       102.6624855  218.2386171    0.47                0.638    
#> rew        31.6666259  144.8718728    0.22                0.827    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Total Sum of Squares:    3070000000
#> Residual Sum of Squares: 855000000
#> R-Squared:      0.722
#> Adj. R-Squared: 0.703
#> F-statistic: 101.612 on 11 and 431 DF, p-value: <0.0000000000000002
#> Oneway (individual) effect Random Effect Model 
#>    (Swamy-Arora's transformation)
#> 
#> Call:
#> plm(formula = deforest ~ agriland + agriexp + foodexp + pop + 
#>     soy + opentr + coal + gas + petrol + nuc + rew, data = merged, 
#>     model = "random", index = c("Country", "Year"))
#> 
#> Unbalanced Panel: n = 19, T = 5-27, N = 461
#> 
#> Effects:
#>                   var std.dev share
#> idiosyncratic 1984360    1409  0.43
#> individual    2641720    1625  0.57
#> theta:
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   0.639   0.826   0.832   0.828   0.835   0.835 
#> 
#> Residuals:
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   -6464    -424    -163      32      64   17100 
#> 
#> Coefficients:
#>                 Estimate   Std. Error z-value             Pr(>|z|)
#> (Intercept) -248.9251724 1082.4344690   -0.23              0.81812
#> agriland       0.0028299    0.0005288    5.35          0.000000087
#> agriexp      -67.2336168  114.9589100   -0.58              0.55865
#> foodexp       49.0166559   26.9428427    1.82              0.06887
#> pop          252.3703544  284.8321410    0.89              0.37560
#> soy           -0.0006119    0.0000354  -17.27 < 0.0000000000000002
#> opentr         1.5762093    6.9863602    0.23              0.82150
#> coal         -62.2733305   34.0530822   -1.83              0.06744
#> gas         -242.6849995  335.2209387   -0.72              0.46909
#> petrol      -119.6844679  186.0710079   -0.64              0.52008
#> nuc         -132.5863143  285.0946361   -0.47              0.64189
#> rew          742.6812194  198.5767435    3.74              0.00018
#>                
#> (Intercept)    
#> agriland    ***
#> agriexp        
#> foodexp     .  
#> pop            
#> soy         ***
#> opentr         
#> coal        .  
#> gas            
#> petrol         
#> nuc            
#> rew         ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Total Sum of Squares:    3760000000
#> Residual Sum of Squares: 1930000000
#> R-Squared:      0.487
#> Adj. R-Squared: 0.475
#> Chisq: 427.819 on 11 DF, p-value: <0.0000000000000002

We can see that agriland, soy are significant in both regression. petrol and opentr are only significant in the fixed effect regression and for the random effect regression rew, coal and foodexp also show to be significant. Both have a similar R-squared.

Just to select which type of model we should go for, we removed all variables that were not significant and a potential source of multicolinerarity. We included the “prod” variable as it includes all the extraction variable. Opentr becomes insignificant when we remove other variable and food exp as well. This model is therefore good to compare both methods. We re-run both regression:

#> Oneway (individual) effect Within Model
#> 
#> Call:
#> plm(formula = deforest ~ agriland + soy + prod, data = merged, 
#>     model = "within", index = c("Country", "Year"))
#> 
#> Unbalanced Panel: n = 68, T = 1-27, N = 1441
#> 
#> Residuals:
#>      Min.   1st Qu.    Median   3rd Qu.      Max. 
#> -10975.96    -28.96     -1.95     25.44  12241.58 
#> 
#> Coefficients:
#>            Estimate Std. Error t-value             Pr(>|t|)    
#> agriland  0.0013648  0.0005980    2.28                0.023 *  
#> soy      -0.0006335  0.0000189  -33.53 < 0.0000000000000002 ***
#> prod     39.1171978  8.5938980    4.55            0.0000058 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Total Sum of Squares:    4430000000
#> Residual Sum of Squares: 2420000000
#> R-Squared:      0.454
#> Adj. R-Squared: 0.426
#> F-statistic: 380.242 on 3 and 1370 DF, p-value: <0.0000000000000002
#> Oneway (individual) effect Random Effect Model 
#>    (Swamy-Arora's transformation)
#> 
#> Call:
#> plm(formula = deforest ~ agriland + soy + prod, data = merged, 
#>     model = "random", index = c("Country", "Year"))
#> 
#> Unbalanced Panel: n = 68, T = 1-27, N = 1441
#> 
#> Effects:
#>                   var std.dev share
#> idiosyncratic 1765646    1329  0.25
#> individual    5222649    2285  0.75
#> theta:
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   0.497   0.877   0.887   0.878   0.889   0.889 
#> 
#> Residuals:
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   -8183    -145     -83       5     -28   16043 
#> 
#> Coefficients:
#>                Estimate  Std. Error z-value             Pr(>|z|)    
#> (Intercept) 588.2809473 328.4888581    1.79              0.07331 .  
#> agriland      0.0016975   0.0003059    5.55          0.000000029 ***
#> soy          -0.0005953   0.0000201  -29.57 < 0.0000000000000002 ***
#> prod         30.5555231   9.0909384    3.36              0.00078 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Total Sum of Squares:    4790000000
#> Residual Sum of Squares: 2960000000
#> R-Squared:      0.383
#> Adj. R-Squared: 0.382
#> Chisq: 892.441 on 3 DF, p-value: <0.0000000000000002

By the random effect model you assume that the intercept are different across countries but that this difference is random and cannot be estimate. In the opposite, the fixed effect model assumes that countries don’t have the same intercept but that this difference can be estimated. In order to know which model we should use between these two we will use the Hausman test:

#> 
#>  Hausman Test
#> 
#> data:  deforest ~ agriland + soy + prod
#> chisq = 33, df = 3, p-value = 0.0000003
#> alternative hypothesis: one model is inconsistent

In fact we can see that the p-value of the Hausman test is very significant. Implying that it is better to use the fixed effect model (model = “within”) and that the random effect model is inconsistent.

#> 
#>  Lagrange Multiplier Test - time effects (Breusch-Pagan) for
#>  unbalanced panels
#> 
#> data:  deforest ~ agriland + agriexp + foodexp + pop + soy + opentr +  ...
#> chisq = 16, df = 1, p-value = 0.00006
#> alternative hypothesis: significant effects

Moreover, by running a Lagrange Multiplier Breusch-Pegan test of our fixed effect panel model, we’ll confirm our intuition that countries behave differently in time (as we cannot reject the null hypothesis stating that countries behave differently) to see whether or not we are right to use the panel data model instead of the OLS. We then solved the problem of heteroskedasticity that was in our previous OLS model.

Now we can look deeper on which extraction factor has the most influence on deforestation. We end up selecting the following model as it is the only significant coefficients that we can get.

Panel fixed effect model

#> Oneway (individual) effect Within Model
#> 
#> Call:
#> plm(formula = deforest ~ agriland + soy + petrol + rew, data = merged, 
#>     model = "within", index = c("Country", "Year"))
#> 
#> Unbalanced Panel: n = 68, T = 1-27, N = 1441
#> 
#> Residuals:
#>      Min.   1st Qu.    Median   3rd Qu.      Max. 
#> -11265.11    -43.81     -1.14     31.89  12174.87 
#> 
#> Coefficients:
#>            Estimate Std. Error t-value             Pr(>|t|)    
#> agriland   0.001262   0.000594    2.13                0.034 *  
#> soy       -0.000679   0.000021  -32.37 < 0.0000000000000002 ***
#> petrol   506.127207 106.296050    4.76            0.0000021 ***
#> rew      177.179701  63.047229    2.81                0.005 ** 
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Total Sum of Squares:    4430000000
#> Residual Sum of Squares: 2380000000
#> R-Squared:      0.462
#> Adj. R-Squared: 0.434
#> F-statistic: 294.22 on 4 and 1369 DF, p-value: <0.0000000000000002

We then have different intercept according to the country. The following table shows the different intercept corresponding to its country:

	x
Albania	-31.90
Angola	2922.86
Argentina	4908.84
Australia	-4325.83
Austria	-113.23
Azerbaijan	-755.41
Bangladesh	-82.88
Belize	101.90
Bhutan	-14.69
Bosnia and Herzegovina	-29.97
Brazil	37366.12
Bulgaria	-73.05
Burkina Faso	361.97
Burundi	-0.97
Cambodia	1312.37
Cameroon	459.58
Canada	-3322.76
China	-6894.28
Colombia	561.04
Croatia	-28.51
Ecuador	111.41
El Salvador	23.51
Ethiopia	302.05
France	-506.15
Georgia	-46.90
Germany	-436.74
Greece	-117.80
Guatemala	507.30
Honduras	164.12
Hungary	-91.10
India	-1593.02
Indonesia	7184.55
Italy	-258.43
Japan	-19.90
Kazakhstan	-3977.91
Madagascar	-67.42
Malawi	390.06
Mali	-503.10
Mexico	-3092.60
Morocco	-386.88
Nepal	76.56
Nicaragua	933.36
Nigeria	-1509.80
North Macedonia	-17.28
Pakistan	-161.72
Panama	90.16
Paraguay	3827.95
Peru	911.82
Philippines	135.07
Poland	-229.00
Romania	-294.74
Rwanda	2.05
Serbia	-3.98
Slovenia	-11.30
South Africa	-785.29
Spain	-467.89
Sri Lanka	34.59
Suriname	42.68
Switzerland	-82.44
Tajikistan	-83.02
Thailand	-328.03
Togo	-18.34
Turkey	-616.51
Uganda	276.83
Ukraine	-290.91
Uruguay	65.28
Zambia	997.16
Zimbabwe	266.02

We don’t forget that those effects are bias from the fact that deforest cannot isolate only the effect of deforestation.

5. Panel with a Balanced data set

We will now try to run our regression with the Balanced data set. This data set differs from merged in the sense that it has the complete set of observations for all the years of the interval. Removing then all the “incomplete” countries.

In order to do so we need first to create a balanced data set from our “merged” data set.

Country	Year	agriland	chagri	chagrip	agriexp	foodexp	forest	deforest	deforestp	land	pop	soy	opentr	prod	coal	gas	petrol	nuclrew	nuc	rew
Albania	1996	11310	40	0.354	9.03	11.09	7771	19.5	-0.003	27400	-0.622	131	48.7	0.083	0.001	0.001	0.022	0.059	NA	0.059
Albania	1997	11350	40	0.352	14.06	11.11	7752	19.5	-0.003	27400	-0.625	76	50.0	0.073	0.000	0.001	0.021	0.051	NA	0.051
Albania	1998	11390	60	0.527	8.76	9.82	7732	19.5	-0.003	27400	-0.629	49	52.6	0.066	0.000	0.001	0.014	0.050	NA	0.050
Albania	1999	11450	-10	-0.087	4.62	5.53	7712	19.5	-0.003	27400	-0.633	523	55.5	0.068	0.000	0.001	0.013	0.053	NA	0.053
Albania	2000	11440	-50	-0.437	5.97	6.63	7693	0.0	0.002	27400	-0.637	292	63.5	0.061	0.000	0.001	0.013	0.046	NA	0.046
Albania	2001	11390	10	0.088	5.54	5.79	7706	0.0	0.002	27400	-0.938	389	66.5	0.051	0.000	0.001	0.014	0.036	NA	0.036
Albania	2002	11400	-190	-1.667	6.52	3.56	7719	0.0	0.002	27400	-0.300	200	68.5	0.051	0.000	0.001	0.015	0.035	NA	0.035

Now that we have it we’ll try to run the fixed effect regression using it.

#> Oneway (individual) effect Within Model
#> 
#> Call:
#> plm(formula = deforest ~ agriland + soy + prod, data = BalancedModel, 
#>     model = "within", index = c("Country", "Year"))
#> 
#> Balanced Panel: n = 40, T = 20, N = 800
#> 
#> Residuals:
#>      Min.   1st Qu.    Median   3rd Qu.      Max. 
#> -11487.74    -69.07     -3.03     41.11  11685.80 
#> 
#> Coefficients:
#>            Estimate Std. Error t-value            Pr(>|t|)    
#> agriland  0.0023185  0.0008775    2.64              0.0084 ** 
#> soy      -0.0006760  0.0000265  -25.51 <0.0000000000000002 ***
#> prod     45.9302896 11.7744632    3.90              0.0001 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Total Sum of Squares:    3100000000
#> Residual Sum of Squares: 1660000000
#> R-Squared:      0.464
#> Adj. R-Squared: 0.434
#> F-statistic: 218.584 on 3 and 757 DF, p-value: <0.0000000000000002

And by looking deeper into the extraction variable, we end up with this model:

Balanced model

#> Oneway (individual) effect Within Model
#> 
#> Call:
#> plm(formula = deforest ~ agriland + soy + gas + petrol, data = BalancedModel, 
#>     model = "within", index = c("Country", "Year"))
#> 
#> Balanced Panel: n = 40, T = 20, N = 800
#> 
#> Residuals:
#>      Min.   1st Qu.    Median   3rd Qu.      Max. 
#> -11570.50    -95.35     -1.65     57.22  11700.20 
#> 
#> Coefficients:
#>             Estimate  Std. Error t-value             Pr(>|t|)    
#> agriland   0.0027167   0.0008874    3.06              0.00228 ** 
#> soy       -0.0007024   0.0000286  -24.59 < 0.0000000000000002 ***
#> gas      747.1629343 206.3489238    3.62              0.00031 ***
#> petrol   341.7205498 162.6322735    2.10              0.03596 *  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Total Sum of Squares:    3100000000
#> Residual Sum of Squares: 1650000000
#> R-Squared:      0.468
#> Adj. R-Squared: 0.438
#> F-statistic: 166.502 on 4 and 756 DF, p-value: <0.0000000000000002

The result of the regression didn’t change much from the unbalanced data set. Nevertheless if we are comparing the countries fixed effect, it’s better to have a balanced data set. As it compares the same year for all countries:

	x
Albania	-35.55
Argentina	3244.63
Australia	-11252.45
Austria	-120.56
Belize	92.15
Brazil	36140.11
Burundi	-34.32
Canada	-7103.84
China	-13603.92
Colombia	-64.75
Croatia	-74.66
Ecuador	121.65
El Salvador	7.85
France	-812.22
Georgia	-74.29
Greece	-217.06
Hungary	-234.77
India	-4458.14
Indonesia	2742.66
Italy	-665.74
Japan	69.90
Kazakhstan	-7024.33
Madagascar	-687.97
Mexico	-4835.41
Morocco	-825.79
Nicaragua	859.16
Pakistan	-1342.46
Panama	64.00
Paraguay	3882.10
Peru	520.35
Philippines	-54.57
Romania	-763.12
Slovenia	-13.92
South Africa	-2233.96
Suriname	45.99
Switzerland	-39.61
Thailand	-1299.88
Turkey	-1109.65
Ukraine	-1411.42
Uruguay	-124.45

6. OLS per year

Another analysis that we can perform in order to compare our results is to apply OLS for one year at a time in order to avoid the heteroskedasticity issue. Here we are going to do this every five years from 1995 until 2015. We will have to make a variable selection for every year.

For this purpose we will use the AIC criteria.

2000

#> Start:  AIC=850
#> deforest ~ agriland + agriexp + foodexp + pop + soy + opentr + 
#>     coal + gas + petrol + rew
#> 
#>            Df Sum of Sq       RSS AIC
#> - foodexp   1    170968 244478503 848
#> - agriexp   1   1107666 245415201 848
#> - pop       1   1293472 245601007 848
#> - opentr    1   4737654 249045189 849
#> - petrol    1   7269416 251576951 849
#> <none>                  244307535 850
#> - agriland  1  84948368 329255903 864
#> - soy       1 111680299 355987834 868
#> - gas       1 132606879 376914414 871
#> - rew       1 169860810 414168345 876
#> - coal      1 374209214 618516749 898
#> 
#> Step:  AIC=848
#> deforest ~ agriland + agriexp + pop + soy + opentr + coal + gas + 
#>     petrol + rew
#> 
#>            Df Sum of Sq       RSS AIC
#> - agriexp   1   1008187 245486690 846
#> - pop       1   2063092 246541596 846
#> - opentr    1   4691041 249169544 847
#> - petrol    1   7124184 251602687 847
#> <none>                  244478503 848
#> - agriland  1  84786811 329265314 862
#> - soy       1 118978000 363456503 867
#> - gas       1 133252625 377731129 869
#> - rew       1 172594396 417072899 874
#> - coal      1 374121726 618600229 896
#> 
#> Step:  AIC=846
#> deforest ~ agriland + pop + soy + opentr + coal + gas + petrol + 
#>     rew
#> 
#>            Df Sum of Sq       RSS AIC
#> - pop       1   3550916 249037606 845
#> - opentr    1   4096307 249582997 845
#> - petrol    1   6467281 251953971 845
#> <none>                  245486690 846
#> - agriland  1  84745434 330232124 860
#> - soy       1 118400506 363887196 865
#> - gas       1 132281474 377768164 867
#> - rew       1 172498198 417984888 873
#> - coal      1 373184642 618671332 894
#> 
#> Step:  AIC=845
#> deforest ~ agriland + soy + opentr + coal + gas + petrol + rew
#> 
#>            Df Sum of Sq       RSS AIC
#> - opentr    1   3564493 252602099 843
#> - petrol    1   7894332 256931938 844
#> <none>                  249037606 845
#> - agriland  1  85300173 334337779 858
#> - soy       1 119878890 368916496 864
#> - gas       1 134644663 383682269 866
#> - rew       1 169742187 418779793 871
#> - coal      1 384359880 633397486 893
#> 
#> Step:  AIC=843
#> deforest ~ agriland + soy + coal + gas + petrol + rew
#> 
#>            Df Sum of Sq       RSS AIC
#> - petrol    1   7124165 259726264 843
#> <none>                  252602099 843
#> - agriland  1  81895163 334497262 857
#> - soy       1 116841426 369443525 862
#> - gas       1 132012614 384614713 864
#> - rew       1 167981563 420583662 869
#> - coal      1 381962883 634564983 891
#> 
#> Step:  AIC=843
#> deforest ~ agriland + soy + coal + gas + rew
#> 
#>            Df Sum of Sq       RSS AIC
#> <none>                  259726264 843
#> - agriland  1  96533561 356259825 858
#> - soy       1 130453075 390179340 863
#> - gas       1 136903263 396629527 864
#> - rew       1 165767561 425493825 868
#> - coal      1 375356522 635082786 889
#> 
#> Call:
#> lm(formula = deforest ~ agriland + soy + coal + gas + rew, data = merged2000)
#> 
#> Coefficients:
#> (Intercept)     agriland          soy         coal          gas  
#>  -290.62442      0.00241      0.00261  -1009.08325  -2337.90840  
#>         rew  
#>  4432.29982

Observations	54
Dependent variable	deforest
Type	OLS linear regression

F(5,48)	46.95
R²	0.83
Adj. R²	0.81

	Est.	S.E.	t val.	p
(Intercept)	-290.62	376.26	-0.77	0.44
agriland	0.00	0.00	4.22	0.00
soy	0.00	0.00	4.91	0.00
coal	-1009.08	121.16	-8.33	0.00
gas	-2337.91	464.79	-5.03	0.00
rew	4432.30	800.79	5.53	0.00
Standard errors: OLS

2005

#> Start:  AIC=896
#> deforest ~ agriland + agriexp + foodexp + pop + soy + opentr + 
#>     coal + gas + petrol + rew
#> 
#>            Df Sum of Sq       RSS AIC
#> - pop       1    204871 201671380 894
#> - foodexp   1    531742 201998251 894
#> - agriexp   1   2375283 203841793 894
#> - opentr    1   3711903 205178412 895
#> <none>                  201466510 896
#> - petrol    1   7246550 208713060 896
#> - agriland  1  53814648 255281158 907
#> - soy       1 106121138 307587648 918
#> - gas       1 130675199 332141708 923
#> - rew       1 145487066 346953575 925
#> - coal      1 345941051 547407560 951
#> 
#> Step:  AIC=894
#> deforest ~ agriland + agriexp + foodexp + soy + opentr + coal + 
#>     gas + petrol + rew
#> 
#>            Df Sum of Sq       RSS AIC
#> - foodexp   1   1087474 202758855 892
#> - agriexp   1   3200144 204871524 892
#> - opentr    1   3513338 205184718 893
#> <none>                  201671380 894
#> - petrol    1   7567944 209239324 894
#> - agriland  1  55377392 257048772 906
#> - soy       1 106880750 308552130 916
#> - gas       1 132038171 333709551 921
#> - rew       1 145747642 347419023 923
#> - coal      1 351183065 552854446 950
#> 
#> Step:  AIC=892
#> deforest ~ agriland + agriexp + soy + opentr + coal + gas + petrol + 
#>     rew
#> 
#>            Df Sum of Sq       RSS AIC
#> - agriexp   1   3169308 205928162 891
#> - opentr    1   3581461 206340316 891
#> <none>                  202758855 892
#> - petrol    1   7225043 209983897 892
#> - agriland  1  54417987 257176842 904
#> - soy       1 117488806 320247661 916
#> - gas       1 131249171 334008026 919
#> - rew       1 145433456 348192311 921
#> - coal      1 350250666 553009520 948
#> 
#> Step:  AIC=891
#> deforest ~ agriland + soy + opentr + coal + gas + petrol + rew
#> 
#>            Df Sum of Sq       RSS AIC
#> - opentr    1   2167937 208096099 889
#> - petrol    1   6999698 212927861 891
#> <none>                  205928162 891
#> - agriland  1  52829459 258757622 902
#> - soy       1 117533712 323461875 915
#> - gas       1 130700665 336628828 917
#> - rew       1 143569574 349497736 919
#> - coal      1 347720074 553648236 946
#> 
#> Step:  AIC=889
#> deforest ~ agriland + soy + coal + gas + petrol + rew
#> 
#>            Df Sum of Sq       RSS AIC
#> <none>                  208096099 889
#> - petrol    1   7332843 215428942 889
#> - agriland  1  50712673 258808773 900
#> - soy       1 117250862 325346961 913
#> - gas       1 129781791 337877890 916
#> - rew       1 141402976 349499076 917
#> - coal      1 350820672 558916771 945
#> 
#> Call:
#> lm(formula = deforest ~ agriland + soy + coal + gas + petrol + 
#>     rew, data = merged2005)
#> 
#> Coefficients:
#> (Intercept)     agriland          soy         coal          gas  
#>  -253.37304      0.00190      0.00143   -623.76018  -2505.59588  
#>      petrol          rew  
#>   376.59273   3974.85188

Observations	58
Dependent variable	deforest
Type	OLS linear regression

F(10,47)	31.12
R²	0.87
Adj. R²	0.84

	Est.	S.E.	t val.	p
(Intercept)	-1287.79	973.02	-1.32	0.19
agriland	0.00	0.00	3.54	0.00
agriexp	21.63	29.06	0.74	0.46
foodexp	4.82	13.70	0.35	0.73
pop	70.11	320.68	0.22	0.83
soy	0.00	0.00	4.98	0.00
opentr	9.14	9.82	0.93	0.36
coal	-636.60	70.86	-8.98	0.00
gas	-2524.16	457.17	-5.52	0.00
petrol	377.50	290.34	1.30	0.20
rew	4119.52	707.11	5.83	0.00
Standard errors: OLS

Observations	58
Dependent variable	deforest
Type	OLS linear regression

F(5,52)	63.73
R²	0.86
Adj. R²	0.85

	Est.	S.E.	t val.	p
(Intercept)	-252.60	314.65	-0.80	0.43
agriland	0.00	0.00	3.89	0.00
soy	0.00	0.00	5.44	0.00
coal	-609.80	66.97	-9.11	0.00
gas	-2217.94	391.99	-5.66	0.00
rew	4093.25	674.52	6.07	0.00
Standard errors: OLS

2010

Observations	62
Dependent variable	deforest
Type	OLS linear regression

F(10,51)	7.31
R²	0.59
Adj. R²	0.51

	Est.	S.E.	t val.	p
(Intercept)	-304.13	830.82	-0.37	0.72
agriland	-0.00	0.00	-0.70	0.49
agriexp	34.66	73.73	0.47	0.64
foodexp	-12.76	11.96	-1.07	0.29
pop	431.02	242.90	1.77	0.08
soy	0.00	0.00	4.59	0.00
opentr	5.72	8.08	0.71	0.48
coal	-67.99	53.41	-1.27	0.21
gas	60.60	360.96	0.17	0.87
petrol	254.19	226.21	1.12	0.27
rew	-88.82	484.92	-0.18	0.86
Standard errors: OLS

#> Start:  AIC=932
#> deforest ~ agriland + agriexp + foodexp + pop + soy + opentr + 
#>     coal + gas + petrol + rew
#> 
#>            Df Sum of Sq       RSS AIC
#> - gas       1     80976 146621183 930
#> - rew       1     96397 146636604 930
#> - agriexp   1    634713 147174919 930
#> - agriland  1   1414602 147954808 930
#> - opentr    1   1440352 147980559 930
#> - foodexp   1   3272957 149813164 931
#> - petrol    1   3628044 150168251 931
#> - coal      1   4656114 151196320 932
#> <none>                  146540206 932
#> - pop       1   9047316 155587522 934
#> - soy       1  60575748 207115954 951
#> 
#> Step:  AIC=930
#> deforest ~ agriland + agriexp + foodexp + pop + soy + opentr + 
#>     coal + petrol + rew
#> 
#>            Df Sum of Sq       RSS AIC
#> - rew       1     56354 146677537 928
#> - agriexp   1    731050 147352232 928
#> - agriland  1   1335612 147956795 928
#> - opentr    1   1393059 148014242 929
#> - foodexp   1   3193763 149814946 929
#> <none>                  146621183 930
#> - coal      1   5324979 151946162 930
#> - petrol    1   5708104 152329287 930
#> - pop       1   9129825 155751008 932
#> - soy       1  68875025 215496208 952
#> 
#> Step:  AIC=928
#> deforest ~ agriland + agriexp + foodexp + pop + soy + opentr + 
#>     coal + petrol
#> 
#>            Df Sum of Sq       RSS AIC
#> - agriexp   1    727189 147404726 926
#> - agriland  1   1297236 147974773 926
#> - opentr    1   1511324 148188861 927
#> - foodexp   1   3146700 149824237 927
#> <none>                  146677537 928
#> - petrol    1   6712240 153389777 929
#> - pop       1   9679070 156356607 930
#> - coal      1  11661642 158339179 931
#> - soy       1 119135427 265812964 963
#> 
#> Step:  AIC=926
#> deforest ~ agriland + foodexp + pop + soy + opentr + coal + petrol
#> 
#>            Df Sum of Sq       RSS AIC
#> - opentr    1   1117455 148522181 925
#> - agriland  1   1607845 149012571 925
#> - foodexp   1   3203654 150608380 926
#> <none>                  147404726 926
#> - petrol    1   6246192 153650918 927
#> - coal      1  11197501 158602227 929
#> - pop       1  11746323 159151049 929
#> - soy       1 121420241 268824967 962
#> 
#> Step:  AIC=925
#> deforest ~ agriland + foodexp + pop + soy + coal + petrol
#> 
#>            Df Sum of Sq       RSS AIC
#> - agriland  1   2191152 150713333 924
#> - foodexp   1   2947514 151469695 924
#> <none>                  148522181 925
#> - petrol    1   6340946 154863127 925
#> - coal      1  10575501 159097682 927
#> - pop       1  10734384 159256565 927
#> - soy       1 120737060 269259241 960
#> 
#> Step:  AIC=924
#> deforest ~ foodexp + pop + soy + coal + petrol
#> 
#>           Df Sum of Sq       RSS AIC
#> - foodexp  1   2211706 152925038 923
#> <none>                 150713333 924
#> - petrol   1   5029659 155742992 924
#> - pop      1   9261200 159974532 925
#> - coal     1  32852682 183566015 934
#> - soy      1 134955558 285668890 961
#> 
#> Step:  AIC=923
#> deforest ~ pop + soy + coal + petrol
#> 
#>          Df Sum of Sq       RSS AIC
#> <none>                152925038 923
#> - pop     1   7131965 160057003 923
#> - petrol  1   8784057 161709095 924
#> - coal    1  33359387 186284425 933
#> - soy     1 136949423 289874462 960
#> 
#> Call:
#> lm(formula = deforest ~ pop + soy + coal + petrol, data = merged2010)
#> 
#> Coefficients:
#> (Intercept)          pop          soy         coal       petrol  
#>    4.957774   311.676234     0.000445   -93.275409   269.155894

Observations	62
Dependent variable	deforest
Type	OLS linear regression

F(4,57)	17.64
R²	0.55
Adj. R²	0.52

	Est.	S.E.	t val.	p
(Intercept)	385.18	249.44	1.54	0.13
agriland	-0.00	0.00	-0.49	0.63
soy	0.00	0.00	6.42	0.00
petrol	344.86	156.92	2.20	0.03
coal	-88.58	35.35	-2.51	0.02
Standard errors: OLS

2015

Observations	60
Dependent variable	deforest
Type	OLS linear regression

F(10,49)	29.26
R²	0.86
Adj. R²	0.83

	Est.	S.E.	t val.	p
(Intercept)	-246.52	545.20	-0.45	0.65
agriland	0.00	0.00	0.24	0.81
agriexp	29.90	43.16	0.69	0.49
foodexp	-15.57	7.54	-2.07	0.04
pop	426.21	145.17	2.94	0.01
soy	0.00	0.00	7.80	0.00
opentr	4.47	4.89	0.91	0.37
coal	-72.92	43.16	-1.69	0.10
gas	-800.40	232.04	-3.45	0.00
petrol	442.36	141.22	3.13	0.00
rew	112.26	262.41	0.43	0.67
Standard errors: OLS

#> Start:  AIC=840
#> deforest ~ agriland + agriexp + foodexp + pop + soy + opentr + 
#>     coal + gas + petrol + rew
#> 
#>            Df Sum of Sq       RSS AIC
#> - agriland  1     56677  50129814 838
#> - rew       1    187044  50260181 838
#> - agriexp   1    490370  50563506 839
#> - opentr    1    852265  50925402 839
#> <none>                   50073137 840
#> - coal      1   2916640  52989776 841
#> - foodexp   1   4362420  54435556 843
#> - pop       1   8808754  58881890 848
#> - petrol    1  10027228  60100365 849
#> - gas       1  12159401  62232537 851
#> - soy       1  62211731 112284868 887
#> 
#> Step:  AIC=838
#> deforest ~ agriexp + foodexp + pop + soy + opentr + coal + gas + 
#>     petrol + rew
#> 
#>           Df Sum of Sq       RSS AIC
#> - rew      1    131813  50261627 836
#> - agriexp  1    447683  50577497 837
#> - opentr   1    795652  50925465 837
#> <none>                  50129814 838
#> - foodexp  1   4714593  54844406 842
#> - coal     1   4799492  54929306 842
#> - pop      1   8898482  59028296 846
#> - petrol   1  10471282  60601095 848
#> - gas      1  12522224  62652037 850
#> - soy      1 106792262 156922076 905
#> 
#> Step:  AIC=836
#> deforest ~ agriexp + foodexp + pop + soy + opentr + coal + gas + 
#>     petrol
#> 
#>           Df Sum of Sq       RSS AIC
#> - agriexp  1    441975  50703602 835
#> - opentr   1    797652  51059279 835
#> <none>                  50261627 836
#> - foodexp  1   4800978  55062605 840
#> - pop      1   8843081  59104708 844
#> - gas      1  12490097  62751724 848
#> - petrol   1  12512199  62773825 848
#> - coal     1  14550360  64811987 850
#> - soy      1 157428513 207690140 919
#> 
#> Step:  AIC=835
#> deforest ~ foodexp + pop + soy + opentr + coal + gas + petrol
#> 
#>           Df Sum of Sq       RSS AIC
#> - opentr   1    650822  51354424 834
#> <none>                  50703602 835
#> - foodexp  1   4459965  55163566 838
#> - pop      1   8828736  59532337 842
#> - petrol   1  12261481  62965083 846
#> - gas      1  12277841  62981443 846
#> - coal     1  14908037  65611639 848
#> - soy      1 157711177 208414779 918
#> 
#> Step:  AIC=834
#> deforest ~ foodexp + pop + soy + coal + gas + petrol
#> 
#>           Df Sum of Sq       RSS AIC
#> <none>                  51354424 834
#> - foodexp  1   4566200  55920624 837
#> - pop      1   8238621  59593045 841
#> - petrol   1  12386234  63740658 845
#> - gas      1  13524818  64879242 846
#> - coal     1  14947366  66301790 847
#> - soy      1 161346828 212701251 917
#> 
#> Call:
#> lm(formula = deforest ~ foodexp + pop + soy + coal + gas + petrol, 
#>     data = merged2015)
#> 
#> Coefficients:
#> (Intercept)      foodexp          pop          soy         coal  
#>  247.758472   -15.421249   376.988059     0.000461   -56.861524  
#>         gas       petrol  
#> -806.270953   459.842415

Observations	60
Dependent variable	deforest
Type	OLS linear regression

F(5,54)	51.45
R²	0.83
Adj. R²	0.81

	Est.	S.E.	t val.	p
(Intercept)	272.78	163.09	1.67	0.10
agriland	0.00	0.00	0.47	0.64
soy	0.00	0.00	10.29	0.00
coal	-65.09	21.15	-3.08	0.00
gas	-952.28	232.36	-4.10	0.00
petrol	599.03	129.02	4.64	0.00
Standard errors: OLS

Resulting coefficients

Now that we have the significant coefficient according to the year, we can compare them. This is an important result as it shows only the coefficients that are significant at 95%. By taking the observations only in one year at a time, we avoid the problem of heteroskedasticity that we had before. Taking one model every five years from 2000 to 2015, we applied the AIC criteria to select the variable. Our four models have a good R-squared and have only significant coefficient. We can see that the coefficient doesn’t vary much through years (except for coal and agriland) so that is a good point. The goal would have been to have all of them on the positive side but when we remove some then the model becomes unsignificant. The year 2005 seems to be a relevant one to be compared with the pooled version of the panel data (OLS) and the fixed effect model that we obtained.

7. Comparison of the Results

Now that we performed all our regression, here is a plot of the coefficients from the main four regression that we made. By ploting the coefficient of the three methods used we can see that the models using the fixed effect panel regression are much closer to zero than the OLS ones. They are more realistic as well because their coefficients are positive. We know that the OLS is bias because of its correlated error term so even if it has given a more complete model we have more incentive to consider the panel fixed effect regression to interpret the coefficient and finally answer to our initial research question.

Pooling regression (OLS)

term	estimate	std.error	statistic	p.value
(Intercept)	-4185.5702	578.4309	-7.24	0.0000
agriland	-0.0022	0.0004	-6.01	0.0000
agriexp	2151.3341	196.8128	10.93	0.0000
foodexp	248.8179	28.3780	8.77	0.0000
pop	777.3459	296.3613	2.62	0.0090
soy	-0.0002	0.0001	-2.17	0.0308
gas	-5379.9487	367.9605	-14.62	0.0000
petrol	1585.3310	212.0827	7.48	0.0000
nuc	-684.4593	266.4543	-2.57	0.0105
rew	3189.2867	274.7572	11.61	0.0000

OLS per year

term	estimate	std.error	statistic	p.value
(Intercept)	-252.5996	314.6505	-0.803	0.4257
agriland	0.0021	0.0005	3.888	0.0003
soy	0.0015	0.0003	5.439	0.0000
coal	-609.7985	66.9664	-9.106	0.0000
gas	-2217.9449	391.9918	-5.658	0.0000
rew	4093.2531	674.5194	6.068	0.0000

Fixed effects with full sample

term	estimate	std.error	statistic	p.value
agriland	0.0013	0.0006	2.13	0.0337
soy	-0.0007	0.0000	-32.37	0.0000
petrol	506.1272	106.2960	4.76	0.0000
rew	177.1797	63.0472	2.81	0.0050

Fixed effects with Balanced data

term	estimate	std.error	statistic	p.value
agriland	0.0027	0.0009	3.06	0.0023
soy	-0.0007	0.0000	-24.59	0.0000
gas	747.1629	206.3489	3.62	0.0003
petrol	341.7205	162.6323	2.10	0.0360

From this we can see the significance level of the different model. For the four regression all our coefficients are significant at 95% (no p-value above 0.05). We can now compare our R-squared.

Model	R-squared
OLS model	0.524
OLS2005	0.859
Fixed effect	0.434
Balanced	0.438

The R-squared is only very high in the ols2005 model. We can therefore conclude the effect on deforestation that were statistically significant at this year and their weight. The last two models have an Rsquared that is quite low but realistic to the fact that there might be an omitted variable that would explain the model better.

8. Answers to the research questions

From the Balanced data set we can first capture the effect of agricultural land on deforestation. According to our results, an increase of 1 square kilometer of the agricultural land implies an increase of deforestation of 0.0027 square kilometers under the same conditions. Also if the soy production decreases by one dollar under ceteris paribus the deforestation increases by 0.0007024 sq km and the increase of one quad of gas decreases the deforestation by 747.1629343 sq km. Regarding the model the increase of one quad of petrol leads to an increase of 341.7205498 deforestation in sq km.

We can make an estimate for the amount of deforestation for a country if we know the amount of agricultural land, soy production in thousand dollar, gas production in quad and petrol production in quad. The estimate amount of deforestation in sq km can be calculated by this formula:

\[ Deforestation = agriculturalland * 0.0027167 - soyproduction * 0.0007024 + gas * 747.1629343 + petrol *341.7205498 \] Because the velue of the R-squared is 0.43 with this model we can just explain approximately 43% of observed variation.

Conclusion

To conclude our report we will quickly go through our main steps and results and explain how it could have been improved.

The main goal of our research was to identify which countries were the most impacted by deforestation, how this differs through time and then detect the possible causes of deforestation not only in a specific region but generally around the globe. For this purpose we used different data sets coming from different sources: the FAO, data.worldbank, ourworldindata and the eia. In each of them we had a deep look into what could be the potential causes of deforestation, selecting then our variables from this data. In order to use this variable we tidied all our data sets and merged them in a single one.

Then we had a deeper look at our data. Exploring their distribution, the extremes values and we managed to map this changes worldwide to visualize better the deforestation. Already in this part we discovered which countries are the most affected by deforestation, like Brazil, Angola, Indonesia, Argentina, Peru, Colombia, Nigeria, Paraguay, Colombia, Zambia and Cambodia. Surprising was the high amount of forest area growth in China and also in India, that was also a problem for the regression we planned to make in the analysis. We verified whether the extremes values we found really faced extreme deforestation (or reforestation) to make sure there was no big mistake in the data we were using. We have also seen that there is a significant positive correlation between deforestation and agricultural land, gas production, petrol production, population growth, production of renewables and soy production and a negative significant correlation between nuclear production and openness to trade. We also discovered some correlation between variables we wanted to use for the regression in our analysis, that was also something we had to take into account.

In the analysis, we tried many different methods and end up showing the four main ones. First the OLS regression, we found it important to show as it is the most common one to analyse and without analyzing it we wouldn’t have been sure whether using it or not was good. We were able to verify whether the assumptions were met or not and unfortunately the error term appeared to be heteroskedastic. In order to deal with it, we used a panel data regression. Nevertheless our results were not completely satisfying as even if the unobservable effect of time was taken into consideration, we had very few significant variables. There could be that an omitted variable was included in the error term. Then by using a balanced model of our data set we end up to very similar result but indicating different extraction causes to deforestation. Finally the result that we had by applying OLS per year was very significant but only for one year. the sign of the resulting coefficient was not in line with our expectation.

To conclude we have to say that we chose a difficult topic to analyse. Starting from the research of the data sets to all the steps from the tyding to the analysis, this subject reveals to be very challenging. Other studies have been done on this subject but they were mostly using satellites pictures to analyse the deforestation. Chosing the forest in square kilometers to determine the difference from one year to the other to set our deforestation variable first seemed to be a great idea to us. But we completely underestimated the effect of “reforestation” applied by many countries and making their forest growing from one year to the next. This effect bias our analysis as our dependent variable contained two opposite effect in one. An alternative to it would have been to take only the value of the primary forest. As primary forest cannot be replanted it would have been a good way to separate those two effects. The data set we found on primary forest was very weak compare to the one we finally end up chosing, therefore we cannot certify that it would have given better result.

Resources

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Ehrhardt-Martinez K., Crenshaw E., Jenkins J.C.: Deforestation and the environmental Kuznets curve: A cross-national investigation of intervening mechanisms. Social Science Quarterly, 83 (1) (2002), pp. 226-243.

W.R. Faria W.R., Almeida A.N: Relationship between openness to trade and deforestation: Empirical evidence from the Brazilian Amazon. Ecological Modelling, 121 (2016), pp. 85-97

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Pfaff A., Robalino J., Walker R., Aldrich S., Caldas M., Reis E., et al.:Road inv